
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

import warnings
warnings.filterwarnings('ignore')


# import dan backup raw dataset
raw_data = pd.read_csv('marketing_campaign.csv', sep=';')
data = raw_data.copy()
data.head()


# menampilkan informasi dataset
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB


# total unique values setiap feature
data.nunique()

ID                     2240
Year_Birth               59
Education                 5
Marital_Status            8
Income                 1974
Kidhome                   3
Teenhome                  3
Dt_Customer             663
Recency                 100
MntWines                776
MntFruits               158
MntMeatProducts         558
MntFishProducts         182
MntSweetProducts        177
MntGoldProds            213
NumDealsPurchases        15
NumWebPurchases          15
NumCatalogPurchases      14
NumStorePurchases        14
NumWebVisitsMonth        16
AcceptedCmp3              2
AcceptedCmp4              2
AcceptedCmp5              2
AcceptedCmp1              2
AcceptedCmp2              2
Complain                  2
Z_CostContact             1
Z_Revenue                 1
Response                  2
dtype: int64


# Z_CostContact adalah jumlah biaya yang digunakan di setiap kampanye, karena semua nilainya sama di semua pelanggan, maka tidak dapat digunakan dalam analisis
# Z_Revenue adalah jumlah pendapatan yang dihasilkan oleh setiap kampanye, karena semua nilainya sama di semua pelanggan, maka tidak dapat digunakan dalam analisis
# ID adalah CustomerID, karena semua pelanggan adalah unik, maka tidak dapat digunakan dalam analisis
data.drop(['ID', 'Z_CostContact', 'Z_Revenue'], axis=1, inplace=True)


# identifikasi missing values
pct_missing_values = data.isnull().sum().sum()/data.shape[0]*100
print(f'Total % Missing Values: {pct_missing_values:.2f}%')

Total % Missing Values: 1.07%


# identifikasi duplicated
pct_duplicates = data[data.duplicated()].shape[0]/data.shape[0]*100
print(f'Total % Duplicated Rows: {pct_duplicates:.2f}%')

Total % Duplicated Rows: 8.12%


# mengubah tipe data Dt_customer dari object menjadi datetime
data['Dt_Customer'] = pd.to_datetime(data['Dt_Customer'])


# memisahkan feature berdasarkan tipe data
numeric_cols = []
object_cols = []
datetime_cols = []

# melakukan perulangan setiap feature sesuai dengan tipe data
for col in data.columns:
    if data[col].dtype == 'int64' or data[col].dtype == 'float64':
        numeric_cols.append(col)
    elif data[col].dtype == 'object':
        object_cols.append(col)
    else:
        datetime_cols.append(col)

# print hasilnya yang disimpan pada masing-masing feature
print(f'Total Numeric Columns : {len(numeric_cols)}')
print(f'Total Object Columns  : {len(object_cols)}')
print(f'Total Datetime Columns: {len(datetime_cols)}')

Total Numeric Columns : 23
Total Object Columns  : 2
Total Datetime Columns: 1


# memisahkan feature berdasarkan klasifikasinya
personal_cols = []
spending_cols = []
purchase_cols = []
campaign_cols = []

# melakukan perulangan setiap feature sesuai dengan klasifikasinya
for col in data.columns:
    if col in 'Year_Birth, Education, Marital_Status, Income, Kidhome, Teenhome, Dt_Customer, Recency, Complain':
        personal_cols.append(col)
    elif col in 'MntWines, MntFruits, MntMeatProducts, MntFishProducts, MntSweetProducts, MntGoldProds':
        spending_cols.append(col)
    elif col in 'NumDealsPurchases, NumWebPurchases, NumCatalogPurchases, NumStorePurchases, NumWebVisitsMonth':
        purchase_cols.append(col)
    elif col in 'AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, Response, NumDealsPurchases':
        campaign_cols.append(col)

# mengurutkan hasilnya sesuai dengan alphabet
personal_cols.sort()
spending_cols.sort()
purchase_cols.sort()
campaign_cols.sort()

# print hasilnya yang disimpan pada masing-masing feature
print(f'Personal Columns: {personal_cols}')
print(f'Spending Columns: {spending_cols}')
print(f'Purchase Columns: {purchase_cols}')
print(f'Campaign Columns: {campaign_cols}')

Personal Columns: ['Complain', 'Dt_Customer', 'Education', 'Income', 'Kidhome', 'Marital_Status', 'Recency', 'Teenhome', 'Year_Birth']
Spending Columns: ['MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntMeatProducts', 'MntSweetProducts', 'MntWines']
Purchase Columns: ['NumCatalogPurchases', 'NumDealsPurchases', 'NumStorePurchases', 'NumWebPurchases', 'NumWebVisitsMonth']
Campaign Columns: ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']


# descriptive statistics pada feature bertipe data numerik
data[numeric_cols].describe(percentiles=list(np.linspace(0.1, 0.9,9))).T


# descriptive statistics pada feature bertipe data object/categorical
data[object_cols].describe().T


# descriptive statistics pada feature bertipe data datetime
data.describe(include='datetime').T


plt.figure(figsize=(20, 10))
for i in range(0, len(numeric_cols)):
    plt.subplot(5, 5, i + 1)
    sns.kdeplot(data[numeric_cols[i]])
    plt.tight_layout()

plt.show()


plt.figure(figsize=(20, 10))
for i in range(0, len(numeric_cols[:-7])):
    plt.subplot(4, 4, i+1)
    sns.boxplot(y=data[numeric_cols[i]])
    plt.tight_layout()


# setup ukuran figure
plt.figure(figsize=(12, 8))

# iterasi untuk setiap Feature objek
for i in range(0, len(object_cols)):
    # mendapatkan urutan kategori berdasarkan jumlah masing-masing
    order = raw_data[object_cols[i]].value_counts().index

    # membuat subplot
    plt.subplot(2, len(object_cols), i + 1)

    # countplot dengan urutan kategori yang diinginkan
    ax = sns.countplot(x=raw_data[object_cols[i]], order=order, palette=sns.color_palette())

    # menambahkan label di atas setiap bar
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', xytext=(0, 5), textcoords='offset points')

    # setup pada xticks
    plt.xticks(rotation=0, ha='center')

plt.tight_layout()
plt.show()


# exclude data yang tidak valid seperti Basic dan 2n Cycle pada Education dan Absurd, Alone, YOLO pada Marital Status
filter_data = (data['Education'].isin(['Graduation', 'Master', 'PhD'])) \
    & (data['Marital_Status'].isin(['Single', 'Married', 'Divorced']))


# 'Education'
education_counts = data[filter_data].groupby(
    'Education')['Response'].value_counts(normalize=True).unstack()
colors = ['#e41a1c', '#377eb8']

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

education_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[0])
axes[0].set_title('Percentage of Response by Education')
axes[0].set_ylabel('Percentage')
axes[0].set_xticklabels(education_counts.index, rotation=0, ha='center')

handles, labels = axes[0].get_legend_handles_labels()
axes[0].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[0].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[0].patches:
    if p.get_height() < 0.5:
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

# 'Marital_Status'
marital_counts = data[filter_data].groupby(
    'Marital_Status')['Response'].value_counts(normalize=True).unstack()
marital_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[1])
axes[1].set_title('Percentage of Response by Marital_Status')
axes[1].set_ylabel('Percentage')
axes[1].set_xticklabels(marital_counts.index, rotation=0, ha='center')

handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[1].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[1].patches:
    if p.get_height() <= 0.5:
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

plt.tight_layout()
plt.show()


# membuat function untuk menampilkan trend distribusi pelanggan yang melakukan registrasi/enrollment setiap bulannya
def monthly_distribution(data, date_col, figsize=(12, 5)):
    # setup subplot untuk line plot
    fig, ax = plt.subplots(figsize=figsize)

    # resample data dan hitung jumlah bulanan
    monthly_counts = data.resample('M', on=date_col).size()

    # plot line distribusi datetime (per bulan)
    ax.plot(monthly_counts.index, monthly_counts.values,
            marker='o', linestyle='-')
    ax.set_title(f'Customer Registration Distribution Trends')
    ax.set_xlabel(date_col)
    ax.set_ylabel('frekuensi')

    # memutar label pada sumbu x agar lebih mudah dibaca
    plt.xticks(rotation=45)

    # membuat indeks bulanan dari Juli 2012 hingga Juni 2014
    monthly_index = pd.date_range(
        start='2012-07-01', end='2014-06-30', freq='M')

    # melakukan reindex dengan nilai pada variabel monthly_index
    monthly_counts = monthly_counts.reindex(monthly_index, fill_value=0)

    # mengubah format xticklabels menjadi nama bulan-tahun
    month_year_labels = [date.strftime('%b-%Y')
                         for date in monthly_counts.index]
    ax.set_xticks(monthly_counts.index)
    ax.set_xticklabels(month_year_labels)

    # menampilkan plot
    plt.tight_layout()
    plt.show()


# memanggil function menampilkan trend pelanggan yang registrasi/enrollment
monthly_distribution(data, 'Dt_Customer')


# membuat function untuk menampilkan trend distribusi pelanggan yang melakukan registrasi/enrollment setiap bulannya bersadarkan response
def monthly_distribution_response(data, date_col, figsize=(12, 5)):

    # setup subplot untuk line plot
    fig, ax = plt.subplots(figsize=figsize)

    # resample data dan hitung jumlah bulanan untuk response = 0
    monthly_counts_0 = data[data['Response'] == 0].resample('M', on='Dt_Customer').size()

    # plot line distribusi datetime (per bulan) untuk response = 0
    ax.plot(monthly_counts_0.index, monthly_counts_0.values,
            marker='o', linestyle='-', label='Response = 0', color = "#ff0000")

    # resample data dan hitung jumlah bulanan untuk response = 1
    monthly_counts_1 = data[data['Response'] == 1].resample('M', on='Dt_Customer').size()

    # plot line distribusi datetime (per bulan) untuk response = 1
    ax.plot(monthly_counts_1.index, monthly_counts_1.values,
            marker='o', linestyle='-', label='Response = 1', color = "#438cc4")

    # Set title, xlabel, dan ylabel
    ax.set_title('Customer Registration Distribution Trends')
    ax.set_xlabel('Dt_Customer')
    ax.set_ylabel('Frekuensi')

    # memutar label pada sumbu x agar lebih mudah dibaca
    plt.xticks(rotation=45)

    # membuat indeks bulanan dari Juli 2012 hingga Juni 2014
    monthly_index = pd.date_range(
        start='2012-07-01', end='2014-06-30', freq='M')

    # melakukan reindex dengan nilai pada variabel monthly_index untuk response = 0
    monthly_counts_0 = monthly_counts_0.reindex(monthly_index, fill_value=0)

    # melakukan reindex dengan nilai pada variabel monthly_index untuk response = 1
    monthly_counts_1 = monthly_counts_1.reindex(monthly_index, fill_value=0)

    # mengubah format xticklabels menjadi nama bulan-tahun
    month_year_labels = [date.strftime('%b-%Y')
                         for date in monthly_index]
    ax.set_xticks(monthly_index)
    ax.set_xticklabels(month_year_labels)

    # Menambah legenda
    ax.legend()

    # menampilkan plot
    plt.tight_layout()
    plt.show()


# memanggil function menampilkan trend pelanggan yang registrasi/enrollment berdasarkan response
monthly_distribution_response(data, 'Dt_Customer')


# regplot setiap feature ke target
plt.figure(figsize=(10, 20))
plt.subplots_adjust(hspace=0.5, wspace=0.5)

for i, col in enumerate(data[numeric_cols[1:-1]]):
    plt.subplot(7, 3, i+1)
    sns.regplot(data=data, x=data[col], y=data['Response'], logistic=True)
    plt.title(f'{col} vs Response')
    plt.xlabel(f'{col}')
    plt.ylabel('Response')

plt.tight_layout()
plt.show()


# menghitung korelasi dengan Response
CORR_RESPONSE = data.corrwith(data['Response'], axis=0, method='pearson', drop=False).sort_values(ascending=False)

# setup figure dan color palette
cmap = sns.diverging_palette(10, 240, as_cmap=True)
plt.figure(figsize=(6, 6))

# heatmap response
sns.heatmap(CORR_RESPONSE.to_frame(), annot=True, fmt=".2f", cbar=True, center=0, cmap=cmap)
plt.title('Correlation to Response')

Text(0.5, 1.0, 'Correlation to Response')


# setup figure dan color palette
cmap = sns.diverging_palette(10, 240, as_cmap=True)
plt.figure(figsize=(12, 8))

# heatmap matrix correlation
sns.heatmap(data.corr(), annot=True, fmt=".2f", cbar=True, center=0, cmap=cmap)
plt.title('Matrix Correlation')

plt.tight_layout()
plt.show()


# menghitung jumlah responden dengan Yes Response tanpa menerima campaign manapun
no_campaign_response = len(data[(data['AcceptedCmp1'] == 0) & (data['AcceptedCmp2'] == 0) & (
    data['AcceptedCmp3'] == 0) & (data['AcceptedCmp4'] == 0) & (data['AcceptedCmp5'] == 0) & (data['Response'] == 1)])

# menghitung jumlah responden dengan Yes Response
total_yes_response = len(data[data['Response'] == 1])

# menghitung jumlah responden dengan Yes Response yang setidaknya menerima satu campaign
at_least_one_campaign_response = total_yes_response - no_campaign_response

# menampilkan hasil
print('Total Yes Response:', total_yes_response)
print('Yes Response without accepting any campaigns:', no_campaign_response)
print('Yes Response accepting at least one campaign:',
      at_least_one_campaign_response)

# menampilkan proporsi dalam persentase
percentage_no_campaign_response = (
    no_campaign_response / total_yes_response) * 100
percentage_at_least_one_campaign_response = (
    at_least_one_campaign_response / total_yes_response) * 100

print('Percentage of Yes Response without accepting any campaigns: {:.1f}%'.format(
    percentage_no_campaign_response))
print('Percentage of Yes Response accepting at least one campaign: {:.1f}%'.format(
    percentage_at_least_one_campaign_response))

Total Yes Response: 334
Yes Response without accepting any campaigns: 146
Yes Response accepting at least one campaign: 188
Percentage of Yes Response without accepting any campaigns: 43.7%
Percentage of Yes Response accepting at least one campaign: 56.3%


# success rate untuk setiap campaign yang diberikan
# inisialisasi dataFrame
campaigns = ['Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5']
df_marketing = pd.DataFrame(
    0, index=campaigns + ['Any campaigns', 'Total Response'], columns=['Count', 'Yes Response', 'Success Rate'])

# menghitung metrik untuk setiap campaign
for col in ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5']:
    count = data[col].sum()
    yes_response = data[(data[col] == 1) & (data['Response'] == 1)].shape[0]
    success_rate = np.round(yes_response / count * 100, 2)

    # mengisi nilai DataFrame
    df_marketing.loc[col[8:], ['Count', 'Yes Response', 'Success Rate']] = [
        count, yes_response, success_rate]

# menghitung metrik untuk 'Any campaigns' dan 'Total Response'
df_marketing.loc['Any campaigns', 'Count'] = len(data) - 1777
df_marketing.loc['Any campaigns', 'Yes Response'] = len(
    data[data['Response'] == 1])
df_marketing.loc['Any campaigns', 'Success Rate'] = np.round(
    df_marketing.loc['Any campaigns', 'Yes Response'] / df_marketing.loc['Any campaigns', 'Count'] * 100, 2)

df_marketing.loc['Total Response', 'Count'] = len(data)
df_marketing.loc['Total Response', 'Yes Response'] = len(
    data[data['Response'] == 1])
df_marketing.loc['Total Response', 'Success Rate'] = np.round(
    df_marketing.loc['Total Response', 'Yes Response'] / len(data) * 100, 2)

# menampilkan dataFrame
df_marketing


# subplots
fig, axes = plt.subplots(2, 1, figsize=(7, 7))
ax = axes.ravel()

# pie chart
colors = ['#377eb8','#e41a1c']
labels = ['Accepting at least one campaign', 'Not accepting any campaign']
sizes = [percentage_at_least_one_campaign_response,
         percentage_no_campaign_response]
ax[0].pie(sizes, labels=labels, autopct='%1.1f%%', colors=colors, startangle=90, textprops={'color': 'white'})
ax[0].set_title('Percentage of Yes Response with and without Accepting Campaigns')

# bar chart for Success Rate
ax[1].bar(df_marketing.index[:-2], df_marketing['Success Rate'][:-2], color='#377eb8', alpha=0.7, label='Success Rate')
ax[1].set_xlabel('Campaigns')
ax[1].set_ylabel('Success Rate (%)')
ax[1].set_title('Success Rate for Each Campaign')

# Menghilangkan garis x dan y
ax[1].spines['top'].set_visible(False)
ax[1].spines['right'].set_visible(False)
ax[1].spines['bottom'].set_visible(True)
ax[1].spines['left'].set_visible(True)

ax[1].tick_params(axis='both', which='both', length=0)  # Menghilangkan tanda tick pada x dan y

# values pada bar chart
for i, v in enumerate(df_marketing['Success Rate'][:-2]):
    ax[1].text(i, v + 1, str(v) + '%', ha='center', va='bottom', fontsize=8)

plt.tight_layout(h_pad=1)
plt.show()


# membuat function untuk menampilkan plot pada agregasi data
def plot_grouped(data, group_column, variables, nrows=1, ncols=1, method='mean', hue=None):

    # melakukan filter pada feature yang bertipe data numerik
    numeric_variables = [
        var for var in variables if pd.api.types.is_numeric_dtype(data[var])]

    # agregasi data berdasarkan hasil feature yang telah terfilter sebelumnya
    grouped_data = data.groupby([group_column, hue]).agg(
        {variable: method for variable in numeric_variables}).reset_index()

    # setup color palette
    palette = sns.color_palette('Set1', len(numeric_variables))

    # setup subplots
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
                             figsize=(10 * ncols, 5 * nrows))

    # untuk memastikan bahwa axes tetap iterable untuk kasus 1 subplot
    if nrows == 1 and ncols == 1:
        axes = [axes]

    # agregasi sesuai dengan methode yang terdefinisi pada parameter
    if method == 'mean':
        title = 'Average'
    elif method == 'sum':
        title = 'Total'
    elif method == 'count':
        title = 'Count'

    # melakukan perulangan pada setiap feature yang ditampilkan
    for i, variable in enumerate(numeric_variables):

        # menentukan posisi axes setiap feature
        row_index = i // ncols
        col_index = i % ncols
        ax = sns.barplot(x=group_column, y=variable, hue=hue,
                         data=grouped_data, ax=axes[row_index][col_index], palette=palette)

        # menentukan title, xlabel, ylabel, dan xticks
        axes[row_index][col_index].set_title(
            f'{title} {variable} by {group_column}')
        axes[row_index][col_index].set_ylabel(variable)
        axes[row_index][col_index].set_xlabel(group_column)
        axes[row_index][col_index].tick_params(axis='x', rotation=0)

        # menambahkan label angka di atas setiap bar
        for p in ax.patches:
            height = p.get_height()
            axes[row_index][col_index].text(p.get_x() + p.get_width()/2., height,
                                            f'{height:.2f}', ha='center', va='bottom', color='black', fontsize=10)

    plt.tight_layout()
    plt.show()


# memilih feature yang akan ditampilkan insightnya
cols_to_plot = ['Income', 'Recency',
                'MntMeatProducts', 'MntWines']

# memanggil function untuk menampilkan barplot pada agregasi feature education
plot_grouped(data[filter_data], 'Education', cols_to_plot,
             nrows=2, ncols=2, hue='Response')


# memanggil function untuk menampilkan barplot pada agregasi marital status
plot_grouped(data[filter_data], 'Marital_Status', cols_to_plot,
             nrows=2, ncols=2, hue='Response')


# melakukan backup dataset sebelum split
data_before_splitting = data.copy()


# restore point
data = data_before_splitting.copy()
data.head()


# split train and test set

# memisahkan antara training dan test set
from sklearn.model_selection import train_test_split
data_train, data_test = train_test_split(data, test_size=0.2, random_state=42)

# menampilkan shape dari train dan test set
print(f'data_train: {data_train.shape}, data_test: {data_test.shape}')

data_train: (1792, 26), data_test: (448, 26)


# backup dataset sebelum handling missing values
data_before_handling_missingvalues_train = data_train.copy()
data_before_handling_missingvalues_test = data_test.copy()


# restore point data_train
data_train = data_before_handling_missingvalues_train.copy()
data.head()


# restore point data_test
data_test = data_before_handling_missingvalues_test.copy()
data.head()


# membuat function untuk mengidentifikasi missing values
def identify_missing_values(data):

    # menghitung missing values pada setiap feature
    missing_values_count = data.isnull().sum().reset_index().rename(
        {'index': 'column', 0: 'missing values'}, axis=1)

    # filter feature yang mempunyai missing values
    missing_values_count = missing_values_count[missing_values_count['missing values'] > 0]

    # menghitng persentase missing values
    missing_values_count['percentage'] = round(
        (missing_values_count['missing values'] / len(data) * 100), 2)

    # mengurutkan jumlah missing values secara descending
    missing_values_count = missing_values_count.sort_values(
        by='missing values', ascending=False).reset_index(drop=True)

    return missing_values_count


# menampilkan data shape dan memanggil function untuk mengidentifikasi missing values
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
print(identify_missing_values(data_train))
print(identify_missing_values(data_test))

Data Train Shape : (1792, 26)
Data Test Shape : (448, 26)
   column  missing values  percentage
0  Income              19        1.06
   column  missing values  percentage
0  Income               5        1.12


# handling missing values, dengan menghapusnya (<10%)
data_train.dropna(axis=0, inplace=True)
data_test.dropna(axis=0, inplace=True)
print(data_train.shape)
print(data_test.shape)

(1773, 26)
(443, 26)


# melakukan backup dataset sebelum handling duplicates
data_before_handling_duplicates_train = data_train.copy()
data_before_handling_duplicates_test = data_test.copy()


# restore point
data_train = data_before_handling_duplicates_train.copy()
data_test = data_before_handling_duplicates_test.copy()


# membuat function untuk mengidentifikasi data terduplikasi
def identify_duplicates(df):

    # mencari baris yang terduplikasi
    duplicate_rows = df[df.duplicated()]

    # menghitng jumlah row yang terduplikasi
    duplicate_count = len(duplicate_rows)

    # membuat dataframe untuk menampilkan jumlah row beserta persentase data yang terduplikasi
    if duplicate_count > 0:
        duplicate_info = pd.DataFrame({
            'duplicated rows': [duplicate_count],
            'percentage': round((duplicate_count / len(df) * 100), 2)
        })
    else:
        duplicate_info = pd.DataFrame({
            'duplicated rows': [0],
            'percentage': [0.0]
        })

    return duplicate_info


# menampikan data shape dan memanggil function untuk mengidentikasi data yang terduplikasi
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
print(identify_duplicates(data_train))
print(identify_duplicates(data_test))

Data Train Shape : (1773, 26)
Data Test Shape : (443, 26)
   duplicated rows  percentage
0              116        6.54
   duplicated rows  percentage
0                6        1.35


# menampilkan baris data yang terduplikasi pada data_train
data_train[data_train.duplicated(keep=False)].sort_values(by=['Education','Marital_Status','Income'])


# menampilkan baris data yang terduplikasi pada data_test
data_test[data_test.duplicated(keep=False)].sort_values(by=['Education','Marital_Status','Income'])


# handling data yang terduplikasi dengan cara menghapusnya
data_train.drop_duplicates(keep='first', inplace=True)
data_test.drop_duplicates(keep='first', inplace=True)
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')

Data Train Shape : (1657, 26)
Data Test Shape : (437, 26)


# melakukan backup dataset sebelum handling outliers
data_before_handling_outliers_train = data_train.copy()
data_before_handling_outliers_test = data_test.copy()


# restore point
data_train = data_before_handling_outliers_train.copy()
data_test = data_before_handling_outliers_test.copy()


# z-score outliers handling
from scipy.stats import zscore
def handle_outliers_zscore(data, columns, threshold=3, plot=False):
    filtered_entries = np.array([True] * len(data))
    outliers_entries = np.array([True] * len(data))

    for column in columns:
        z_scores = np.abs(zscore(data[column]))
        filtered = (z_scores < threshold)
        filtered_entries = np.logical_and(filtered, filtered_entries)
        outliers_entries = ~filtered_entries
        filtered_data = data[filtered_entries]
        outliers_data = data[outliers_entries]

    filtered_data.reset_index(drop=True, inplace=True)
    outliers_data.reset_index(drop=True, inplace=True)

    if plot:
        for column in columns:
            fig, ax = plt.subplots(1, 2, figsize=(8, 4))
            ax[0].set_title(f'Before Outliers in {column}')
            sns.boxplot(data[column], ax=ax[0])
            ax[1].set_title(f'After Outliers in {column}')
            sns.boxplot(filtered_data[column], ax=ax[1])
            plt.tight_layout()
            plt.show()

    data_size = data.shape[0]
    filtered_size = filtered_data.shape[0]
    outliers_size = outliers_data.shape[0]
    pct_outliers = round((outliers_size / data_size) * 100, 2)

    result = pd.Series(data=[data_size, filtered_size, outliers_size, pct_outliers],
                       index=['Before', 'After', 'Outliers', '% Outliers'])
    print(result, '\n')

    return filtered_data, outliers_data


# menghapus outlier pada feature income data_train
outlier_cols = ['Income']
data_train, residu_income_train = handle_outliers_zscore(data_train, outlier_cols, 3, True)

# menghapus outlier pada feature Year_Birth data_train
outlier_cols = ['Year_Birth']
data_train, residu_yearbirth_train = handle_outliers_zscore(data_train, outlier_cols, 3, True)

Before        1657.00
After         1650.00
Outliers         7.00
% Outliers       0.42
dtype: float64

Before        1650.00
After         1649.00
Outliers         1.00
% Outliers       0.06
dtype: float64


residu_income_train.sort_values(by='Income', ascending=False)


residu_yearbirth_train.sort_values(by='Year_Birth', ascending=False)


# handling invalid values
# mengubah value pada feature education
data_train['Education'].replace({'Basic':'Graduation','2n Cycle':'Master'}, inplace=True)
data_test['Education'].replace({'Basic':'Graduation','2n Cycle':'Master'}, inplace=True)

# mengubah value pada feature marital_status
data_train['Marital_Status'].replace({'YOLO':'Single','Absurd':'Single','Alone':'Single',
                                'Widow':'Divorced','Together':'Married'}, inplace=True)
data_train['Marital_Status'].replace({'YOLO':'Single','Absurd':'Single','Alone':'Single',
                                'Widow':'Divorced','Together':'Married'}, inplace=True)


# backup dataset sebelum melakukan feature extraction
data_before_extraction_train = data_train.copy()
data_before_extraction_test = data_test.copy()
data_before_extraction = data.copy()


# restore point
data_train = data_before_extraction_train.copy()
data_test = data_before_extraction_test.copy()
data = data_before_extraction.copy()


data_train['feature'] = 'train'
data_test['feature'] = 'test'
data = pd.concat([data_train,data_test]).reset_index(drop=True)


# membuat feature baru berdasarkan status hubungan
marital = {
    'Single': 'Not in relationship',
    'Together': 'In relationship',
    'Married': 'In relationship',
    'Divorced': 'Not in relationship',
    'Widow': 'Not in relationship',
    'Alone': 'Not in relationship',
    'Absurd': 'Not in relationship',
    'YOLO': 'Not in relationship'
}
data['Relationship_Status'] = data['Marital_Status'].map(marital)

# membuat feature baru total_children dari penjumlahan feature kidhome dan teenhome
data['Total_Children'] = data['Kidhome'] + data['Teenhome']

# membuat feature baru berdasarkan jumlah anggota keluarga
def fam_size(x):
    if x['Relationship_Status'] == 'Not in relationship':
        result = 1 + x['Teenhome'] + x['Kidhome']
    elif x['Relationship_Status'] == 'In relationship':
        result = 2 + x['Teenhome'] + x['Kidhome']
    return result
data['Family_Size'] = data.apply(fam_size, axis=1)

# membuat feature baru berdasarkan tanggal bergabung dan diasumsikan data dikumpulkan pada awal juli 2014
data['Customer_Lifespan'] = (pd.to_datetime('2014-07-01') - data['Dt_Customer']).dt.days

# ekstraksi feature Datetime menjadi feature baru
data['Year'] = data['Dt_Customer'].dt.year
data.drop(['Dt_Customer'],axis=1,inplace=True)

# membuat feature baru total purchase, total spending, dan total offers
data['Total_Purchase'] = data.apply(lambda x: x[purchase_cols[:-1]].sum(), axis=1)
data['Total_Spending'] = data.apply(lambda x: x[spending_cols].sum(), axis=1)
data['Total_Offers'] = data.apply(lambda x: x[campaign_cols[:-1]].sum(), axis=1)

# membuat feature baru untuk menghitung ratio spending dengan total pembelian, dan ratio pembelian diskon dengan total pembelian
data['Spending_Purchase_Ratio'] = data['Total_Spending']/data['Total_Purchase']
data['Deal_Purchase_Ratio'] = data['NumDealsPurchases']/data['Total_Purchase']

# membuat feature baru accept one campaign and more than one campaign
data['at_least_one_campaign'] = data.apply(lambda x: 1 if x[campaign_cols[:-1]].sum() == 1 else 0, axis=1)
data['more_one_campaign'] = data.apply(lambda x: 1 if x[campaign_cols[:-1]].sum() > 1 else 0, axis=1)

# membuat feature baru untuk primary_needs
def categorize_needs(row):
    primary_needs = row['MntFruits'] + row['MntMeatProducts'] + row['MntFishProducts']
    secondary_needs = row['MntWines'] + row['MntGoldProds'] + row['MntSweetProducts']

    if primary_needs > secondary_needs:
        return 'primary_needs'
    else:
        return 'secondary_needs'

data['primary_needs'] = data.apply(categorize_needs, axis=1)

# identifikasi dan handling inf value dari 'Spending_Purchase_Ratio' dan 'Deal_Purchase_Ratio'
data.replace([np.inf, -np.inf], np.nan, inplace=True)
data.dropna(axis=0, inplace=True)
data.reset_index(drop=True, inplace=True)
data.head()


# rfm encode

'''
Champions: Pelanggan dengan RFM score tertinggi.
Loyal Customers: Pelanggan dengan frekuensi tinggi dan nilai monetary tinggi.
At-Risk Customers: Pelanggan dengan recency rendah dan frekuensi rendah.
New Customers: Pelanggan dengan recency tinggi dan frekuensi rendah.
'''

# membuat feature baru category rfm score (champions, loyal, at risk, new)
rfm = pd.DataFrame()
rfm['Recency'] = data['Recency']
rfm['Frequency'] = data['Total_Purchase']
rfm['Monetary'] = data['Total_Spending']
rfm.head()


# kalkulasi score berdasarkan quantile masing-masing feature
rfm['recency_score'] = pd.qcut(rfm['Recency'], q=[0, 0.25, 0.5, 0.75, 1], labels=[4, 3, 2, 1])
rfm['frequency_score'] = pd.qcut(rfm['Frequency'], q=[0, 0.25, 0.5, 0.75, 1], labels=[1, 2, 3, 4])
rfm['monetary_score'] = pd.qcut(rfm['Monetary'], q=[0, 0.25, 0.5, 0.75, 1], labels=[1, 2, 3, 4])

# histplot rfm features
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
sns.histplot(rfm['recency_score'], kde=True, ax=axes[0], color='#e9c369')
axes[0].set_title('Recency Score')

sns.histplot(rfm['frequency_score'], kde=True, ax=axes[1], color='#e9c369')
axes[1].set_title('Frequency Score')

sns.histplot(rfm['monetary_score'], kde=True, ax=axes[2], color='#e9c369')
axes[2].set_title('Monetary Score')

plt.tight_layout()
plt.show()


# histplot rfm score
rfm['rfm_score'] = rfm['recency_score'].astype('int') + rfm['frequency_score'].astype('int') + rfm['monetary_score'].astype('int')
sns.histplot(rfm['rfm_score'], kde=True, bins=10, color='#e9c369')

<Axes: xlabel='rfm_score', ylabel='Count'>


'''scoring rfm
0-4 new cust
5-7 at risk cust
8-10 loyal cust
10-12 champions

'''
rfm['RFM_Cat'] = pd.cut(rfm['rfm_score'], bins=[0,4,7,10,12], labels=['new cust','at risk cust','loyal cust','champions'])
ax = sns.countplot(x=rfm['RFM_Cat'], palette=sns.color_palette())
for p in ax.patches:
    ax.annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
    ha='center', va='center', xytext=(0, 5), textcoords='offset points')


# concat dataframe data dengan rfm
data = pd.concat([data, rfm['RFM_Cat']], axis=1)


# backup dataset sebelum melakukan feature enconding
data_before_encoding = data.copy()
data_before_encoding.to_csv('data_before_encoding.csv', index=False)


# restore point
data = data_before_encoding.copy()


from sklearn.preprocessing import OrdinalEncoder
# encoding education
edu = {'Graduation': 1, 'Master': 2, 'PhD': 3}
oe_edu = OrdinalEncoder(categories=[list(edu.keys())])
data['Education'] = oe_edu.fit_transform(data[['Education']])

# encoding marital_status
marital_mapping = {'Single': 1, 'Married': 2, 'Divorced': 3}
data['Marital_Status'] = data['Marital_Status'].map(marital_mapping)

# encoding relationship_status
rel_mapping = {'Not in relationship': 0,
               'In relationship': 1}
data['Relationship_Status'] = data['Relationship_Status'].map(
    rel_mapping)

# encoding primary_needs
pr_mapping = {'primary_needs': 0,
              'secondary_needs': 1}
data['primary_needs'] = data['primary_needs'].map(pr_mapping)

# encoding rfm_cat
rfm_mapping = {'new cust': 1, 'at risk cust': 2,
               'loyal cust': 3, 'champions': 4}
oe_rfm = OrdinalEncoder(categories=[list(rfm_mapping.keys())])
data['RFM_Cat'] = oe_rfm.fit_transform(data[['RFM_Cat']])


# splitting dataset / restore data_train dan data_test
data_train = data[data['feature'] == 'train'].copy()
data_test = data[data['feature'] == 'test'].copy()

data_train.drop('feature', axis=1, inplace=True)
data_test.drop('feature', axis=1, inplace=True)

data_train.reset_index(drop=True, inplace=True)
data_test.reset_index(drop=True, inplace=True)


# backup data sebelum transformation
data_before_transform_train = data_train.copy()
data_before_transform_test = data_test.copy()


# restore point data train dan data test
data_train = data_before_transform_train.copy()
data_test = data_before_transform_test.copy()


from scipy.stats import skew, kurtosis
def assess_distribution_multi(data, features, alpha=0.05, show_plot=False):

    num_features = len(features)
    num_cols = min(4, num_features)  # Maksimal 4 Feature dalam satu baris
    num_rows = (num_features - 1) // num_cols + 1  # Jumlah baris yang dibutuhkan

    if show_plot:
        fig, axes = plt.subplots(num_rows, num_cols, figsize=(16, 4 * num_rows))
        axes = axes.flatten()

    skew_type_list = []  # Tambahkan inisialisasi list
    skew_val_list = []   # Tambahkan inisialisasi list
    normalization_advice_list = []  # Tambahkan inisialisasi list
    transform_columns = []  # Tambahkan inisialisasi list

    results = []
    for i, feature_name in enumerate(features):
        feature_data = data[feature_name].dropna(axis=0)
        skew_val = round(skew(feature_data, nan_policy="omit"), 3)

        if (len(feature_data) > 0):  # Tambahkan kondisi untuk memastikan ada data sebelum melanjutkan
            mean = round(feature_data.mean(), 3)
            median = feature_data.median()
            mode = feature_data.mode()[0]
            q1 = feature_data.quantile(q=0.25)
            q3 = feature_data.quantile(q=0.75)

            if (mean == median == mode) or (-0.2 < skew_val < 0.2):
                skew_type = "Normal Distribution (Symmetric)"
                normalization_advice = "No Transformation"

            elif mean < median < mode:
                skew_type = "Negatively Skewed"
                normalization_advice = "MinMaxScaler"
                if skew_val <= -1:
                    skew_type = "Highly Negatively Skewed"
                    normalization_advice = "PowerTransformer/Log"
            else:
                skew_type = "Positively Skewed"
                normalization_advice = "MinMaxScaler"
                if skew_val >= 1:
                    skew_type = "Highly Positively Skewed"
                    normalization_advice = "PowerTransformer/Log"

            skew_type_list.append(skew_type)
            skew_val_list.append(skew_val)
            normalization_advice_list.append(normalization_advice)

            # Tambahkan fitur ke dalam daftar yang perlu ditransformasi atau standarisasi
            if normalization_advice != "No Transformation":
                transform_columns.append(feature_name)

            # Gambar histplot dalam subplot
            if show_plot:
                sns.histplot(feature_data, kde=True, ax=axes[i])
                axes[i].set_title(f'Histogram for {feature_name}')

    if show_plot:
        # Sembunyikan subplot yang tidak terpakai
        for j in range(i + 1, len(axes)):
            axes[j].axis('off')

        plt.tight_layout()
        plt.show()

    # Buat DataFrame hasil penilaian distribusi
    result_df = pd.DataFrame({
        'Feature': features,
        'Skewness': skew_val_list,
        'Type of Distribution': skew_type_list,
        'Normalization Advice': normalization_advice_list
    }).set_index('Feature')

    return result_df, transform_columns


# memilih feature yang akan ditransformasikan
cols_to_transform = data_train.drop('Response', axis=1).columns
cols_to_transform = sorted(cols_to_transform)


# identifikasi feature sebelum ditransformasi
result_table = assess_distribution_multi(data_train, cols_to_transform, show_plot=True)[0]
result_table


log_cols = list(result_table[result_table['Normalization Advice'] == 'PowerTransformer/Log'].index)
norm_cols = list(result_table[result_table['Normalization Advice'].isin(['MinMaxScaler','No Transformation'])].index)

print(f'log transformation: {log_cols}')
print(f'normalisasi: {norm_cols}')

log transformation: ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Complain', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntMeatProducts', 'MntSweetProducts', 'MntWines', 'NumCatalogPurchases', 'NumDealsPurchases', 'NumWebPurchases', 'Spending_Purchase_Ratio', 'Total_Offers', 'at_least_one_campaign', 'more_one_campaign']
normalisasi: ['Customer_Lifespan', 'Deal_Purchase_Ratio', 'Education', 'Family_Size', 'Income', 'Kidhome', 'Marital_Status', 'NumStorePurchases', 'NumWebVisitsMonth', 'RFM_Cat', 'Recency', 'Relationship_Status', 'Teenhome', 'Total_Children', 'Total_Purchase', 'Total_Spending', 'Year', 'Year_Birth', 'primary_needs']


# menggunakan powertransformer untuk transformasi features pada log_cols
from sklearn.preprocessing import PowerTransformer

# melakukan transformasi
scaler = PowerTransformer(method='yeo-johnson')
data_train[log_cols] = scaler.fit_transform(data_train[log_cols])
data_test[log_cols] = scaler.transform(data_test[log_cols])

# menampilkan distribusi fitur-fitur setelah PowerTransform
plt.figure(figsize=(12, 6))
plt.boxplot(data_train[log_cols], vert=False, labels=data_train[log_cols].columns)
plt.title('Distribusi Fitur setelah PowerTransform')
plt.xlabel('Nilai Transformasi')
plt.ylabel('Fitur')
plt.show()


# menggunakan powertransformer untuk transformasi features
from sklearn.preprocessing import MinMaxScaler

minmax = MinMaxScaler()
data_train[norm_cols] = minmax.fit_transform(data_train[norm_cols])
data_test[norm_cols] = minmax.transform(data_test[norm_cols])

# menampilkan distribusi fitur-fitur setelah Normalisasi
plt.figure(figsize=(12, 6))
plt.boxplot(data_train[norm_cols], vert=False,
            labels=data_train[norm_cols].columns)
plt.title('Distribusi Fitur setelah Normalisasi')
plt.xlabel('Nilai Normalisasi')
plt.ylabel('Fitur')
plt.show()


# backup
data_before_feature_selection_train = data_train.copy()
data_before_feature_selection_test = data_test.copy()


# restore
data_train = data_before_feature_selection_train.copy()
data_train.head()


# memisahkan feature berdasarkan tipe data cat/object dan numerik
num_cols = []
cat_cols = []

# melakukan perulangan setiap feature sesuai dengan tipe data
for col in data_before_encoding.drop(['Response','feature'], axis=1).columns:
    if data_before_encoding[col].dtype == 'int64' or data_before_encoding[col].dtype == 'float64':
        num_cols.append(col)
    elif data_before_encoding[col].dtype == 'object' or data_before_encoding[col].dtype == 'category':
        cat_cols.append(col)

# print hasilnya yang disimpan pada masing-masing feature
print(f'Total Numeric Columns : {len(num_cols)}')
print(f'Total Object Columns  : {len(cat_cols)}')

Total Numeric Columns : 33
Total Object Columns  : 5


# memisahkan X dan y sebagai feature dan target
X_train = data_train.drop('Response', axis=1)
y_train = data_train['Response']

X_test = data_test.drop('Response', axis=1)
y_test = data_test['Response']


# melakukan features selection menggunakan f_classif (anova)
from sklearn.feature_selection import f_classif

# menghitung nilai F-statistic dan p-value untuk setiap feature
f_statistic, p_values = f_classif(X_train[num_cols], y_train)

# menentukan tingkat signifikansi (alpha)
alpha = 0.05

# memilih feature yang memiliki p-value kurang dari 0.05 (95%, tingkat signifikansi umumnya digunakan)
selected_features_indices = np.where(p_values < alpha)[0]
selected_feature_names = X_train[num_cols].columns[selected_features_indices].tolist()

# membuat DataFrame dengan informasi F-statistic dan p-value untuk fitur yang dipilih
selected_features_anova = pd.DataFrame({
    'Feature': selected_feature_names,
    'F_Statistic': f_statistic[selected_features_indices],
    'P_Value': p_values[selected_features_indices]
})
# mengurutkan DataFrame berdasarkan F-statistic secara descending
selected_features_anova = selected_features_anova.sort_values(by='F_Statistic', ascending=False)

# menampilkan feature yang dipilih beserta skor F-statistic dalam bentuk tabel
print("\nTabel Feature yang Dipilih:")
print(selected_features_anova)

# menampilkan skor dalam bentuk plot
plt.figure(figsize=(12, 6))
plt.bar(selected_features_anova['Feature'], -np.log10(selected_features_anova['P_Value']), color='#e9c369')
plt.axhline(-np.log10(alpha), color='red', linestyle='dashed', linewidth=2, label=f'Significance Level ({alpha})')
plt.title('Feature Importance based on ANOVA F-Statistic')
plt.xlabel('Features')
plt.ylabel('-log10(p-value)')
plt.xticks(rotation=45, ha='right')
plt.legend()
plt.show()

Tabel Feature yang Dipilih:
                    Feature  F_Statistic       P_Value
23             Total_Offers   292.373006  1.835470e-60
27        more_one_campaign   246.157183  8.332075e-52
14             AcceptedCmp5   212.448003  2.363466e-45
15             AcceptedCmp1   179.074224  7.643053e-39
22           Total_Spending   126.699997  2.281261e-28
12             AcceptedCmp3   118.700317  9.721559e-27
11      NumCatalogPurchases   102.398177  2.159944e-23
24  Spending_Purchase_Ratio    98.001782  1.750366e-22
18              Family_Size    95.648147  5.379007e-22
6           MntMeatProducts    93.846780  1.271728e-21
3                   Recency    80.756569  6.823586e-19
26    at_least_one_campaign    66.710987  6.192657e-16
9              MntGoldProds    65.667004  1.030405e-15
4                  MntWines    64.506086  1.816035e-15
19        Customer_Lifespan    59.696130  1.911319e-14
17           Total_Children    59.358758  2.255200e-14
13             AcceptedCmp4    54.314835  2.691016e-13
10          NumWebPurchases    53.451504  4.118075e-13
0                    Income    52.134165  7.887214e-13
20                     Year    46.463783  1.305505e-11
21           Total_Purchase    45.046183  2.639741e-11
2                  Teenhome    44.137642  4.147375e-11
16             AcceptedCmp2    39.731354  3.733437e-10
5                 MntFruits    30.385701  4.104349e-08
8          MntSweetProducts    28.313325  1.173656e-07
25      Deal_Purchase_Ratio    20.639837  5.946699e-06
7           MntFishProducts    18.802021  1.538053e-05
1                   Kidhome    14.569459  1.400886e-04


X_train_anova = X_train.loc[:,selected_feature_names]
X_test_anova = X_test.loc[:,selected_feature_names]


# melakukan features selection menggunakan chi-square test
from sklearn.feature_selection import chi2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# menghitung chi-square statistic dan p-value untuk setiap feature
chi2_stat, p_values_chi2 = chi2(X_train[cat_cols], y_train)

# menentukan tingkat signifikansi (alpha)
alpha = 0.05

# memilih feature yang memiliki p-value kurang dari 0.05 (95% confidence level)
selected_features_chi2_indices = np.where(p_values_chi2 < alpha)[0]
selected_features_chi2_names = X_train[cat_cols].columns[selected_features_chi2_indices].tolist(
)

# membuat DataFrame dengan informasi Chi-statistic dan p-value untuk fitur yang dipilih
selected_features_chi = pd.DataFrame({
    'Feature': selected_features_chi2_names,
    'Chi_Statistic': chi2_stat[selected_features_chi2_indices],
    'P_Value': p_values_chi2[selected_features_chi2_indices]
})
# mengurutkan DataFrame berdasarkan Chi-statistic secara descending
selected_features_chi = selected_features_chi.sort_values(
    by='Chi_Statistic', ascending=False)

# menampilkan feature yang dipilih beserta skor Chi-statistic dalam bentuk tabel
print("\nTabel Feature yang Dipilih:")
print(selected_features_chi)

# menampilkan skor dalam bentuk plot
plt.figure(figsize=(12, 6))
plt.bar(selected_features_chi['Feature'], -
        np.log10(selected_features_chi['P_Value']), color='#e9c369')
plt.axhline(-np.log10(alpha), color='red', linestyle='dashed',
            linewidth=2, label=f'Significance Level ({alpha})')
plt.title('Feature Importance based on Chi-square Statistic')
plt.xlabel('Features')
plt.ylabel('-log10(p-value)')
plt.xticks(rotation=0, ha='center')
plt.ylim(0, 10)  # Mengatur skala ylim dari 0 hingga 10
plt.legend()
plt.show()

Tabel Feature yang Dipilih:
               Feature  Chi_Statistic   P_Value
2              RFM_Cat      19.379965  0.000011
1  Relationship_Status      13.154564  0.000287
0            Education       4.374336  0.036484


X_train_chi2 = X_train.loc[:,selected_features_chi2_names]
X_test_chi2 = X_test.loc[:,selected_features_chi2_names]


# concat selected features from anova dan chi2
X_train = pd.concat([X_train_anova, X_train_chi2], axis=1)
X_test = pd.concat([X_test_anova, X_test_chi2], axis=1)


# default feature selection
from statsmodels.stats.outliers_influence import variance_inflation_factor

def calculate_vif(df):
    vif_data = pd.DataFrame()
    vif_data["Feature"] = df.columns
    vif_data["VIF"] = [variance_inflation_factor(
        df.values, i) for i in range(df.shape[1])]
    return vif_data

vif_results = calculate_vif(X_train)
print(f'Avg. VIF Score: {(sum(vif_results["VIF"]/len(vif_results)))}')
vif_results

Avg. VIF Score: inf


drop_features = ['Total_Children', 'Kidhome', 'Teenhome', 'at_least_one_campaign', 'more_one_campaign', 'Family_Size', 'Relationship_Status',
                 'Income', 'Year', 'Total_Purchase', 'Spending_Purchase_Ratio', 'Total_Spending', 'Deal_Purchase_Ratio', 'Total_Offers','MntMeatProducts','MntWines']


# vif score setelah menghapus beberapa feature
vif_results = calculate_vif(X_train.drop(drop_features, axis=1))
print(f'Avg. VIF Score: {(sum(vif_results["VIF"]/len(vif_results)))}')
vif_results

Avg. VIF Score: 2.2793386425396354


# membuat variabel untuk feature selection
selected_features = X_train.drop(drop_features, axis=1).columns
selected_features = sorted(list(selected_features))
selected_features

['AcceptedCmp1',
 'AcceptedCmp2',
 'AcceptedCmp3',
 'AcceptedCmp4',
 'AcceptedCmp5',
 'Customer_Lifespan',
 'Education',
 'MntFishProducts',
 'MntFruits',
 'MntGoldProds',
 'MntSweetProducts',
 'NumCatalogPurchases',
 'NumWebPurchases',
 'RFM_Cat',
 'Recency']


# backup sebelum handling imbalance
X_before_handling_imbalance_training = X_train.copy()
y_before_handling_imbalance_training = y_train.copy()


# restore point
X_train = X_before_handling_imbalance_training.copy()
y_train = y_before_handling_imbalance_training.copy()


X_train = X_train[selected_features]
X_test = X_test[selected_features]


# melakukan imbalace handling pada target
from imblearn.over_sampling import SMOTE

# menampikan jumlah kelas sebelum oversampling
print("Jumlah kelas sebelum oversampling:")
print("Kelas 0:", sum(y_train == 0))
print("Kelas 1:", sum(y_train == 1))

# melakukan oversampling dengan SMOTE
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)

# menampilkan jumlah kelas setelah oversampling
print("\nJumlah kelas setelah oversampling:")
print("Kelas 0:", sum(y_resampled == 0))
print("Kelas 1:", sum(y_resampled == 1))

Jumlah kelas sebelum oversampling:
Kelas 0: 1397
Kelas 1: 251

Jumlah kelas setelah oversampling:
Kelas 0: 1397
Kelas 1: 1397


# mendefinisikan X (features) dan y (target), dimana X dan y hasil dari imbalance handling sebelumnya
X_train = X_resampled
y_train = y_resampled


from sklearn.model_selection import learning_curve, cross_val_score, GridSearchCV
from sklearn.metrics import precision_score, accuracy_score, make_scorer, log_loss
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.ensemble import AdaBoostClassifier


X_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2794 entries, 0 to 2793
Data columns (total 15 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   AcceptedCmp1         2794 non-null   float64
 1   AcceptedCmp2         2794 non-null   float64
 2   AcceptedCmp3         2794 non-null   float64
 3   AcceptedCmp4         2794 non-null   float64
 4   AcceptedCmp5         2794 non-null   float64
 5   Customer_Lifespan    2794 non-null   float64
 6   Education            2794 non-null   float64
 7   MntFishProducts      2794 non-null   float64
 8   MntFruits            2794 non-null   float64
 9   MntGoldProds         2794 non-null   float64
 10  MntSweetProducts     2794 non-null   float64
 11  NumCatalogPurchases  2794 non-null   float64
 12  NumWebPurchases      2794 non-null   float64
 13  RFM_Cat              2794 non-null   float64
 14  Recency              2794 non-null   float64
dtypes: float64(15)
memory usage: 327.5 KB


def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    # Learning Curve for Precision
    axes[0].set_title(f"{title} - Precision")
    if ylim is not None:
        axes[0].set_ylim(*ylim)
    axes[0].set_xlabel("Training examples")
    axes[0].set_ylabel("Score")

    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=make_scorer(precision_score, average='weighted'))

    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)

    axes[0].grid()
    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,
                         train_scores_mean + train_scores_std, alpha=0.1,
                         color="r")
    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,
                         test_scores_mean + test_scores_std, alpha=0.1, color="g")
    axes[0].plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
    axes[0].plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
    axes[0].legend(loc="best")

    # Learning Curve for Loss
    axes[1].set_title(f"{title} - Loss")
    if ylim is not None:
        axes[1].set_ylim(*ylim)
    axes[1].set_xlabel("Training examples")
    axes[1].set_ylabel("Loss")

    train_sizes, train_loss, test_loss = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=make_scorer(log_loss, needs_proba=True))

    train_loss_mean = np.mean(train_loss, axis=1)
    train_loss_std = np.std(train_loss, axis=1)
    test_loss_mean = np.mean(test_loss, axis=1)
    test_loss_std = np.std(test_loss, axis=1)

    axes[1].grid()
    axes[1].fill_between(train_sizes, train_loss_mean - train_loss_std,
                         train_loss_mean + train_loss_std, alpha=0.1,
                         color="r")
    axes[1].fill_between(train_sizes, test_loss_mean - test_loss_std,
                         test_loss_mean + test_loss_std, alpha=0.1, color="g")
    axes[1].plot(train_sizes, train_loss_mean, 'o-', color="r", label="Training loss")
    axes[1].plot(train_sizes, test_loss_mean, 'o-', color="g", label="Cross-validation loss")
    axes[1].legend(loc="best")

    plt.tight_layout()
    return plt


def models_precision(X_train, y_train, X_test, y_test):
    models = [
        SVC(probability=True, random_state=42),
        XGBClassifier(max_depth=1, random_state=42),
        AdaBoostClassifier(random_state=42)
    ]

    results_list_test = []
    results_list_train = []
    results_list_diff = []
    results_list_diff_pct = []

    for model in models:
        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='precision')
        avg_cv_precision = cv_scores.mean()

        model.fit(X_train, y_train)

        # Learning Curve
        # plot_learning_curve(model, f"{type(model).__name__}", X_train, y_train, cv=5)

        # test set evaluation
        y_pred_test = model.predict(X_test)
        precision_test = precision_score(y_test, y_pred_test, average='weighted')

        results_test = {
            "Model": type(model).__name__,
            "CV Precision": avg_cv_precision,
            "Precision": precision_test
        }
        results_list_test.append(results_test)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        precision_train = precision_score(y_train, y_pred_train, average='weighted')

        results_train = {
            "Model": type(model).__name__,
            "Precision": precision_train
        }
        results_list_train.append(results_train)

        # diff evaluation
        results_diff = {
            "Model": type(model).__name__,
            "Precision_Difference": precision_train - precision_test
        }
        results_list_diff.append(results_diff)

        # diff evaluation pct
        results_diff_pct = {
            "Model": type(model).__name__,
            "Precision_Difference": ((precision_train - precision_test) / precision_train) * 100
        }
        results_list_diff_pct.append(results_diff_pct)

    # dataframe from test and test evaluation
    results_test_df = pd.DataFrame(results_list_test)
    results_test_df.set_index("Model", inplace=True)

    results_train_df = pd.DataFrame(results_list_train)
    results_train_df.set_index("Model", inplace=True)

    # dataframe evaluations diff
    results_diff_df = pd.DataFrame(results_list_diff)
    results_diff_df.set_index("Model", inplace=True)

    # dataframe evaluation diff pct
    results_diff_pct_df = pd.DataFrame(results_list_diff_pct)
    results_diff_pct_df.set_index("Model", inplace=True)

    # new df
    evaluation_df = pd.DataFrame(index=[type(model).__name__ for model in models])
    evaluation_df['CV Precision'] = results_test_df['CV Precision']
    evaluation_df['Precision_Train'] = results_train_df['Precision']
    evaluation_df['Precision_Test'] = results_test_df['Precision']
    evaluation_df['Diff'] = results_diff_df['Precision_Difference']
    evaluation_df['Diff (%)'] = results_diff_pct_df['Precision_Difference']

    return evaluation_df


def models_accuracy(X_train, y_train, X_test, y_test):
    models = [
        SVC(probability=True, random_state=42),
        XGBClassifier(max_depth=1, random_state=42),
        AdaBoostClassifier(random_state=42)
    ]

    results_list_test = []
    results_list_train = []
    results_list_diff = []
    results_list_diff_pct = []

    for model in models:
        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
        avg_cv_accuracy = cv_scores.mean()

        model.fit(X_train, y_train)

        # Learning Curve
        # plot_learning_curve(model, f"{type(model).__name__}", X_train, y_train, cv=5)

        # test set evaluation
        y_pred_test = model.predict(X_test)
        accuracy_test = accuracy_score(y_test, y_pred_test)

        results_test = {
            "Model": type(model).__name__,
            "CV Accuracy": avg_cv_accuracy,
            "Accuracy": accuracy_test
        }
        results_list_test.append(results_test)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        accuracy_train = accuracy_score(y_train, y_pred_train)

        results_train = {
            "Model": type(model).__name__,
            "Accuracy": accuracy_train
        }
        results_list_train.append(results_train)

        # diff evaluation
        results_diff = {
            "Model": type(model).__name__,
            "Accuracy_Difference": accuracy_train - accuracy_test
        }
        results_list_diff.append(results_diff)

        # diff evaluation pct
        results_diff_pct = {
            "Model": type(model).__name__,
            "Accuracy_Difference": ((accuracy_train - accuracy_test) / accuracy_train) * 100
        }
        results_list_diff_pct.append(results_diff_pct)

    # dataframe from test and test evaluation
    results_test_df = pd.DataFrame(results_list_test)
    results_test_df.set_index("Model", inplace=True)

    results_train_df = pd.DataFrame(results_list_train)
    results_train_df.set_index("Model", inplace=True)

    # dataframe evaluations diff
    results_diff_df = pd.DataFrame(results_list_diff)
    results_diff_df.set_index("Model", inplace=True)

    # dataframe evaluation diff pct
    results_diff_pct_df = pd.DataFrame(results_list_diff_pct)
    results_diff_pct_df.set_index("Model", inplace=True)

    # new df
    evaluation_df = pd.DataFrame(index=[type(model).__name__ for model in models])
    evaluation_df['CV Accuracy'] = results_test_df['CV Accuracy']
    evaluation_df['Accuracy_Train'] = results_train_df['Accuracy']
    evaluation_df['Accuracy_Test'] = results_test_df['Accuracy']
    evaluation_df['Diff'] = results_diff_df['Accuracy_Difference']
    evaluation_df['Diff (%)'] = results_diff_pct_df['Accuracy_Difference']

    return evaluation_df


def model_tuning_precision(X_train, y_train, X_test, y_test, params_list, model_type='svc'):
    results_list = []

    for idx, params in enumerate(params_list):
        param_name = f"param_{idx+1}"

        if model_type.lower() == 'svc':
            model = SVC(**params, probability=True, random_state=42)
        elif model_type.lower() == 'xgbclassifier':
            model = XGBClassifier(**params, max_depth=1, random_state=42)
        elif model_type.lower() == 'adaboost':
            model = AdaBoostClassifier(**params, random_state=42)
        else:
            raise ValueError("Invalid model type. Supported types are 'svc', 'xgbclassifier', and 'adaboost'.")

        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='precision')
        avg_cv_precision = cv_scores.mean()

        model.fit(X_train, y_train)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        precision_train = precision_score(y_train, y_pred_train, average='weighted')

        # test set evaluation
        y_pred_test = model.predict(X_test)
        precision_test = precision_score(y_test, y_pred_test, average='weighted')

        results = {
            "Parameter Set": param_name,
            "Model": type(model).__name__,
            "CV Precision": avg_cv_precision,
            "Precision_Train": precision_train,
            "Precision_Test": precision_test,
            "Diff": precision_train - precision_test,
            "Diff (%)": ((precision_train - precision_test) / precision_train) * 100,
            "Parameters": str(params)
        }
        results_list.append(results)

    # Create DataFrame from results list
    evaluation_df = pd.DataFrame(results_list)
    evaluation_df.set_index("Parameter Set", inplace=True)

    return evaluation_df


def model_tuning_accuracy(X_train, y_train, X_test, y_test, params_list, model_type='svc'):
    results_list = []

    for idx, params in enumerate(params_list):
        param_name = f"param_{idx+1}"

        if model_type.lower() == 'svc':
            model = SVC(**params, probability=True, random_state=42)
        elif model_type.lower() == 'xgbclassifier':
            model = XGBClassifier(**params, max_depth=1, random_state=42)
        elif model_type.lower() == 'adaboost':
            model = AdaBoostClassifier(**params, random_state=42)
        else:
            raise ValueError("Invalid model type. Supported types are 'svc', 'xgbclassifier', and 'adaboost'.")

        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
        avg_cv_accuracy = cv_scores.mean()

        model.fit(X_train, y_train)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        accuracy_train = accuracy_score(y_train, y_pred_train)

        # test set evaluation
        y_pred_test = model.predict(X_test)
        accuracy_test = accuracy_score(y_test, y_pred_test)

        results = {
            "Parameter Set": param_name,
            "Model": type(model).__name__,
            "CV Accuracy": avg_cv_accuracy,
            "Accuracy_Train": accuracy_train,
            "Accuracy_Test": accuracy_test,
            "Diff": accuracy_train - accuracy_test,
            "Diff (%)": ((accuracy_train - accuracy_test) / accuracy_train) * 100,
            "Parameters": str(params)
        }
        results_list.append(results)

    # Create DataFrame from results list
    evaluation_df = pd.DataFrame(results_list)
    evaluation_df.set_index("Parameter Set", inplace=True)

    return evaluation_df


# first metric (precision)
default_precision = models_precision(X_train, y_train, X_test, y_test)
default_precision


# second metric (accuracy)
default_accuracy = models_accuracy(X_train, y_train, X_test, y_test)
default_accuracy


# precision tuning
# define model and parameters
model = SVC(probability=True)
kernel = ['poly', 'rbf', 'linear']
C = [1, 2, 3, 4, 5]
gamma = ['scale', 'auto']

# define grid search
grid = dict(kernel=kernel,C=C,gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")

Best: 0.874959 using {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8644953563104041, 0.8929407815319182, 0.01856815437095068 with: {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
0.7993784604063702, 0.8210307612871779, 0.012063588854681057 with: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
0.7922502890434228, 0.793926676438712, 0.003878999368544625 with: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
0.8648059951888861, 0.892933514050925, 0.017988902787741075 with: {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
0.8008227825365651, 0.8226624910842218, 0.01080446522967414 with: {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
0.7922502890434228, 0.793926676438712, 0.003878999368544625 with: {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
0.8697615356892001, 0.8999971848486954, 0.02252521870767744 with: {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
0.818454516247957, 0.844862851865334, 0.010662542618646867 with: {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
0.7908714261187912, 0.7949764167038232, 0.00413311469160671 with: {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
0.8737489307337661, 0.9026723433894415, 0.025167587428961368 with: {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
0.8221844859344621, 0.8460160592299794, 0.013148893449438722 with: {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
0.7908714261187912, 0.7949764167038232, 0.00413311469160671 with: {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
0.8745567055442045, 0.9058455806482624, 0.021897984490693974 with: {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
0.8294839492880092, 0.8574063148341029, 0.013612934368169327 with: {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
0.7905774249647815, 0.7943927795369439, 0.00341690076545919 with: {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
0.8749594226083375, 0.9088298610927522, 0.02410831557897364 with: {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8304118173766311, 0.8595460877422555, 0.015466872742696048 with: {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
0.7905774249647815, 0.7943927795369439, 0.00341690076545919 with: {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
0.8719363631409017, 0.90922576634777, 0.024107700968200516 with: {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
0.8363044171567899, 0.867403054422895, 0.014970522763672054 with: {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
0.7899151733091522, 0.795143331065292, 0.0040912185497245625 with: {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
0.8715389911950513, 0.9104298741100247, 0.02191057611185297 with: {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
0.8384168740408899, 0.8698470491527861, 0.01785773479953582 with: {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
0.7899151733091522, 0.795143331065292, 0.0040912185497245625 with: {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
0.8707849019506877, 0.9109999360143208, 0.020548024900351028 with: {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
0.8402555911334215, 0.8724592736138066, 0.016895992603648068 with: {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
0.7910988656371538, 0.79503972135662, 0.004456659072693344 with: {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
0.8718026094639788, 0.9132210382199716, 0.02051176454686488 with: {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
0.843024860734786, 0.8747285893185079, 0.01649835504382095 with: {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.7910988656371538, 0.79503972135662, 0.004456659072693344 with: {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}


# evaluation results - precision
svc_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='svc')
svc_tuning_precision


# filter parameter - precision
filter = (svc_tuning_precision['CV Precision'] > 0.8) & (svc_tuning_precision['Diff'] > 0)
svc_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])


# get parameter
params_svc_precision = svc_tuning_precision.loc['param_10']
params_svc_precision['Parameters']

"{'C': 2, 'gamma': 'auto', 'kernel': 'poly'}"


# Define XGBClassifier model and parameters
model = XGBClassifier(max_depth=1)
objective = ['reg:squarederror','binary:logistic','reg:logistic']
learning_rate = [0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
subsample = [0.8, 0.9, 1.0]
colsample_bytree = [0.8, 0.9, 1.0]
gamma = [1,2,3]

# Define grid search
grid = dict(objective=objective, learning_rate=learning_rate, n_estimators=n_estimators, subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")

Best: 0.910658 using {'colsample_bytree': 1.0, 'gamma': 2, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8299457744641299, 0.8389208943404004, 0.014888192993427939 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8293090267767853, 0.8398972118929592, 0.008221253825128958 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8293335554208031, 0.8373391538639574, 0.010104068645862965 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8228856680385596, 0.8342904887499492, 0.01706061828633937 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8216838405322011, 0.8355145170747902, 0.016266046642534795 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8189310282017626, 0.8326457004936584, 0.01001571499292632 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8228856680385596, 0.8342904887499492, 0.01706061828633937 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8216838405322011, 0.8355145170747902, 0.016266046642534795 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8189310282017626, 0.8326457004936584, 0.01001571499292632 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8548548879714687, 0.8700321016290957, 0.009469492081913793 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8534811698500115, 0.8674051385767629, 0.014341680290427889 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8471510688632365, 0.8677982753797234, 0.012090710654316427 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.850585282221234, 0.8662002276131936, 0.01307394721397286 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8487908939834, 0.8656186629733265, 0.0150989689456641 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8471510688632365, 0.8677982753797234, 0.012090710654316427 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.850585282221234, 0.8662002276131936, 0.01307394721397286 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8487908939834, 0.8656186629733265, 0.0150989689456641 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8596845384698699, 0.8778725872576445, 0.012406523522338451 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8648407858816329, 0.8741077790023116, 0.017637774236310313 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8661294095456397, 0.8897400255519325, 0.011462317919958533 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.863519233775935, 0.8883583257668981, 0.010654815888523876 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8642082748775876, 0.886151598000745, 0.01270817284923519 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8661294095456397, 0.8897400255519325, 0.011462317919958533 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.863519233775935, 0.8883583257668981, 0.010654815888523876 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8642082748775876, 0.886151598000745, 0.01270817284923519 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8591720436679118, 0.8754161605611447, 0.014354792164482617 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8593776333250485, 0.8779163674446764, 0.015304647200532345 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8762556598440427, 0.8993492725355271, 0.014485364396322344 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8741487752764157, 0.8969663784863509, 0.008327512976452695 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8775306507444981, 0.8969020397272317, 0.012578463327935566 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8762556598440427, 0.8993492725355271, 0.014485364396322344 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8741487752764157, 0.8969663784863509, 0.008327512976452695 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8775306507444981, 0.8969020397272317, 0.012578463327935566 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8441669953392429, 0.8680072902230132, 0.013975592420518136 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8496433140324123, 0.867069446070054, 0.010961405434728205 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8536886567174257, 0.8655302303782955, 0.022044685976612174 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8439706434297121, 0.8604704936404348, 0.01172551390006844 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8454180241200246, 0.8640410858374634, 0.009959145502240283 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8470019608247317, 0.8637802803761472, 0.010307379866323194 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8439706434297121, 0.8604704936404348, 0.01172551390006844 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8454180241200246, 0.8640410858374634, 0.009959145502240283 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8470019608247317, 0.8637802803761472, 0.010307379866323194 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8605313488639631, 0.8823385619382991, 0.012064355291686583 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8558489108977888, 0.8813514411107656, 0.011852378568149177 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8539318407930129, 0.8675435713961932, 0.022251484958428155 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8698913904382868, 0.8965322345994353, 0.011813190840288602 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8665162674123212, 0.8927107287360421, 0.005561550612255409 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8711928731217929, 0.8931201656828819, 0.012043186907673321 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8698913904382868, 0.8965322345994353, 0.011813190840288602 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8665162674123212, 0.8927107287360421, 0.005561550612255409 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8711928731217929, 0.8931201656828819, 0.012043186907673321 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8608036471761903, 0.8876859601209862, 0.016059140464401742 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8653469491549867, 0.8846714264130462, 0.014325072986277538 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8539318407930129, 0.8675435713961932, 0.022251484958428155 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8866675883737527, 0.9124757149094045, 0.00787109168509897 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8876701193025814, 0.9114277762742697, 0.014200411587414094 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8872344933073361, 0.9069516008212537, 0.017368551800109093 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8866675883737527, 0.9124757149094045, 0.00787109168509897 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8876701193025814, 0.9114277762742697, 0.014200411587414094 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8872344933073361, 0.9069516008212537, 0.017368551800109093 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.866499995751259, 0.8870388869399075, 0.018987774616259376 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8606203412776086, 0.8830119235153919, 0.009995285398294272 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8539318407930129, 0.8675435713961932, 0.022251484958428155 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8997842381354406, 0.9188517976755453, 0.012324260037845206 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8919343958978176, 0.9209177012262177, 0.01043414123927923 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8947810640280185, 0.9169970389729656, 0.010342907403135752 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8997842381354406, 0.9188517976755453, 0.012324260037845206 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8919343958978176, 0.9209177012262177, 0.01043414123927923 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8947810640280185, 0.9169970389729656, 0.010342907403135752 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8570606338460462, 0.8827852002835318, 0.009655904819475212 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8667034095831501, 0.881214685454484, 0.020110121307498337 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8578005387212431, 0.8765733900875926, 0.028928680947540295 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8571008506718207, 0.8847291050900349, 0.019226422514316232 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8571198272994955, 0.884873066724453, 0.013307147885574883 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8642407920437213, 0.8857759864916515, 0.012610336160429999 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8571008506718207, 0.8847291050900349, 0.019226422514316232 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
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0.9076538543403861, 0.9390165047775716, 0.017881679646635718 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8974378783432557, 0.930424879822716, 0.01594900940566984 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}


xgb_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='xgbclassifier')
xgb_tuning_precision


# filter parameter - precision
filter = (xgb_tuning_precision['CV Precision'] > 0.8) & (xgb_tuning_precision['Diff'] > 0)
xgb_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])


# get parameter
params_xgb_precision = xgb_tuning_precision.loc['param_140']
params_xgb_precision['Parameters']

"{'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}"


# Initialize XGBoost model
params = {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
xgb_model = XGBClassifier(**params, max_depth=1, random_state=42)

# Perform cross-validation with precision as the scoring metric
precision_scores = cross_val_score(xgb_model, X_train, y_train, cv=5, scoring='precision')

# Print mean and standard deviation of precision scores
print("Mean Precision:", np.mean(precision_scores))
print("Standard Deviation of Precision:", np.std(precision_scores))

# Fit the model on the entire training data
xgb_model.fit(X_train, y_train)

# Feature importances
importance_xgb = pd.Series(xgb_model.feature_importances_, index=X_train.columns).sort_values(ascending=True)

# Plotting (using barh for horizontal bar plot)
ax = importance_xgb.plot.barh(align="center", color='#e9c369')
ax.set_title("XGB Classifier Feature Importances")
ax.figure.tight_layout()
plt.show()

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)

Mean Precision: 0.9124665259093071
Standard Deviation of Precision: 0.016099837171287733


# Define AdaBoostClassifier model and parameters
model = AdaBoostClassifier(random_state=42)
learning_rate = [0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
algorithm = ['SAMME', 'SAMME.R']

# Define grid search
grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, algorithm=algorithm)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")

Best: 0.908271 using {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
0.6492129025796463, 0.6483929230586379, 0.016845244587131936 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 50}
0.6481665781698287, 0.6536249086884554, 0.015391772893054802 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 100}
0.6978192522770351, 0.7011184776316066, 0.023461272691963353 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 150}
0.7143417916689243, 0.7218193697536923, 0.02187046474265906 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 200}
0.7741031546092639, 0.7910114003195097, 0.020136252906739694 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 50}
0.7920923098178263, 0.808250360899665, 0.022840059522987156 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 100}
0.7897202908849246, 0.8101847186221949, 0.019488280391017073 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 150}
0.7981894861638571, 0.8122734741370502, 0.017207304914300448 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 200}
0.7922655606464397, 0.8067944593762009, 0.026223375631359194 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 50}
0.7926709666042697, 0.8075427745566068, 0.01956882844555728 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 100}
0.8053032028562879, 0.8193491631051957, 0.02114187021855238 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 150}
0.8088078920584441, 0.8261938141175433, 0.022245577041637588 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 200}
0.8027496143255508, 0.8162847569153773, 0.014800250522407958 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 50}
0.8115298990560648, 0.8218829459586697, 0.02169416571727745 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 100}
0.8235857476444908, 0.8342885774868224, 0.015784528624701036 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 150}
0.8332554594576814, 0.8443920380169654, 0.02022584692235086 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 200}
0.8109491608928519, 0.8256750451810884, 0.016063004696718102 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 50}
0.8383133802442699, 0.8459384336022439, 0.020463931825548558 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 100}
0.8520541924726521, 0.868237492388252, 0.01909883243025282 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 150}
0.8554486452478848, 0.8741264281055463, 0.016249676363641396 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 200}
0.8385508869975627, 0.8510792469833289, 0.01507930171908834 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 50}
0.8608792877132567, 0.8861656732332917, 0.019810730521170997 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 100}
0.8663340131964483, 0.8880402808701776, 0.015449005182397132 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 150}
0.8780231974140461, 0.8941777917310516, 0.01620907660722986 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 200}
0.8492118876912322, 0.8684037807169277, 0.017496843397156774 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}
0.8632976683046565, 0.8880285672993706, 0.01868966734811718 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 100}
0.8821573122055067, 0.9032788502681613, 0.017446611247299147 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 150}
0.8827708932514792, 0.9072559009234059, 0.015755686953888043 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 200}
0.6490939587056719, 0.6534428171807594, 0.014436633770513825 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 50}
0.7099828256270964, 0.7174151620720879, 0.015419428150022596 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 100}
0.7229098710982346, 0.7319493649714768, 0.016061284278344252 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 150}
0.7423688566704706, 0.752377811059427, 0.019765496022307358 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 200}
0.7852388342132902, 0.7950171406858104, 0.016860003031678637 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 50}
0.8017634695936557, 0.8132264625833647, 0.009684776139796762 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}
0.8183604486250415, 0.8312745791243936, 0.010758529494339204 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 150}
0.8296336991140434, 0.8440187844352256, 0.013945072807370496 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 200}
0.7995899610490653, 0.813977564473379, 0.007451251330903823 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 50}
0.833774227844047, 0.8464073695128302, 0.016464257032295194 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 100}
0.8492455343764036, 0.8632262232467707, 0.014447931352461764 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 150}
0.8582696093958649, 0.8786438595274436, 0.016366243283525 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 200}
0.8224710072356645, 0.835541500387432, 0.010516732917087757 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 50}
0.84998718027335, 0.8684274308355654, 0.014484054528523504 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 100}
0.8664717361345332, 0.8882350871717314, 0.01875774503066364 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 150}
0.8764391092845185, 0.8979913560745288, 0.01639436499593488 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 200}
0.8428472052663828, 0.863361847485766, 0.017747013628598252 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 50}
0.8706493507552402, 0.89498604155281, 0.012884893159994764 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 100}
0.8862269073301036, 0.9081727144945514, 0.014684080395021903 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 150}
0.8955467113070703, 0.915046486262384, 0.013327170691222683 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 200}
0.8605723896741674, 0.885645353691421, 0.017953642566500748 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 50}
0.8911474857423715, 0.9105714212160256, 0.01755219172638399 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 100}
0.8989724265285923, 0.9219293146720087, 0.01560168887937801 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 150}
0.9001824427320889, 0.9288254631291938, 0.013488662528999029 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 200}
0.8724634280697028, 0.8948320894885159, 0.015675246369269036 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 50}
0.8929582286758455, 0.9172776025228355, 0.018418534128540092 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.9032525488063451, 0.9242118947069929, 0.019295069138344296 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.9082709380712878, 0.9358567904100219, 0.02648982805265192 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}


adb_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='adaboost')
adb_tuning_precision


# filter parameter - precision
filter = (adb_tuning_precision['CV Precision'] > 0.8) & (adb_tuning_precision['Diff'] > 0)
adb_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])


# get parameter
params_adb_precision = adb_tuning_precision.loc['param_56']
params_adb_precision['Parameters']

"{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}"


# Initialize Adaboost with DecisionTreeClassifier as base estimator
params = {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adaboost_model = AdaBoostClassifier(**params, random_state=42)

# Perform cross-validation with precision as the scoring metric
precision_scores = cross_val_score(adaboost_model, X_train, y_train, cv=5, scoring='precision')

# Print mean and standard deviation of precision scores
print("Mean Precision:", np.mean(precision_scores))
print("Standard Deviation of Precision:", np.std(precision_scores))

# Fit the model on the entire training data
adaboost_model.fit(X_train, y_train)

# Feature importances
importance_adb = pd.Series(adaboost_model.feature_importances_, index=X_train.columns).sort_values(ascending=True)

# Plotting (using barh for horizontal bar plot)
ax = importance_adb.plot.barh(align="center", color='#e9c369')
ax.set_title("Adaboost Feature Importances")
ax.figure.tight_layout()
plt.show()

Mean Precision: 0.9082709380712878
Standard Deviation of Precision: 0.02648982805265192


# accuracy tuning
# define model and parameters
model = SVC(probability=True)
kernel = ['poly', 'rbf', 'linear']
C = [1, 2, 3, 4, 5]
gamma = ['scale', 'auto']

# define grid search
grid = dict(kernel=kernel,C=C,gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")

Best: 0.872946 using {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.771293464391739, 0.788385539854246, 0.01600741715998074 with: {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
0.8325017151723829, 0.851914892765525, 0.01823377390974064 with: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
0.8060117593500941, 0.8078022435397182, 0.007238425945776351 with: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
0.777734177134027, 0.794380785439003, 0.013134682520941026 with: {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
0.8342912651239732, 0.8537044818767935, 0.016771402944234844 with: {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
0.8060117593500941, 0.8078022435397182, 0.007238425945776351 with: {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
0.794916036701483, 0.8137073233202466, 0.010315091279536347 with: {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
0.8507562788132932, 0.8716895783057794, 0.016993674201332572 with: {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
0.8045787087797592, 0.8086970180851873, 0.006991492873876753 with: {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
0.8009996088765782, 0.817197056104501, 0.01250702581961604 with: {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
0.8532607510852073, 0.8733001164591613, 0.018802791600030466 with: {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
0.8045787087797592, 0.8086970180851873, 0.006991492873876753 with: {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
0.8042196446547534, 0.8246237888847535, 0.012067969072769765 with: {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
0.8600611691384386, 0.8838585601485555, 0.019320435290910504 with: {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
0.8049364905328897, 0.8082496308124527, 0.008066089430052357 with: {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
0.8070825398657357, 0.8281133215673562, 0.012325743005626664 with: {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8614929373368984, 0.885648149259824, 0.01984938016979657 with: {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
0.8049364905328897, 0.8082496308124527, 0.008066089430052357 with: {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
0.8077987445579342, 0.8306188343678589, 0.01238102989712471 with: {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
0.8665031642526015, 0.8911958074701948, 0.01919770484314012 with: {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
0.8042209270266284, 0.8088759489820829, 0.007702322075514746 with: {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
0.8113784856470527, 0.8360770071196167, 0.013880934002412498 with: {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
0.867577150697931, 0.8930749220604067, 0.021445271075986268 with: {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
0.8042209270266284, 0.8088759489820829, 0.007702322075514746 with: {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
0.8113772032751777, 0.8396558251591808, 0.014442193893815264 with: {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
0.8693673418354588, 0.8960274219303406, 0.02111147735680577 with: {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
0.8052942722860201, 0.8088759489820829, 0.009070098513506958 with: {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
0.8171029936971422, 0.845024672533647, 0.01799443680801489 with: {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
0.8729458005527022, 0.8976380801447135, 0.02269325086550227 with: {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.8052942722860201, 0.8088759489820829, 0.009070098513506958 with: {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}


# evaluation results - accuracy
svc_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='svc')
svc_tuning_accuracy


# filter parameter - accuracy
filter = (svc_tuning_accuracy['CV Accuracy'] > 0.8) & (svc_tuning_accuracy['Diff (%)'] < 15)
svc_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])


# get parameter
params_svc_accuracy = svc_tuning_precision.loc['param_5']
params_svc_accuracy['Parameters']

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)

"{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}"


# Define XGBClassifier model and parameters
model = XGBClassifier(max_depth=1)
objective = ['reg:squarederror','binary:logistic','reg:logistic']
learning_rate = [0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
subsample = [0.8, 0.9, 1.0]
colsample_bytree = [0.8, 0.9, 1.0]
gamma = [1,2,3]

# Define grid search
grid = dict(objective=objective, learning_rate=learning_rate, n_estimators=n_estimators, subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")

Best: 0.882618 using {'colsample_bytree': 1.0, 'gamma': 2, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8342906239380359, 0.8498574475833724, 0.03424037737398742 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8324985092426953, 0.8499473332452887, 0.03417726205834697 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8339309186270928, 0.8477994821369256, 0.03606534289675377 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
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0.8572021210430814, 0.8886908949746471, 0.06439941212476472 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.5, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8242733760363168, 0.8476208313823422, 0.03475575790615557 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8242663229910043, 0.8462793098894238, 0.03349374627235041 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.814249075089285, 0.8405517602942295, 0.03271757163572124 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8518360359320599, 0.8809954256762437, 0.05900057057293238 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8443200543725675, 0.8826064440735892, 0.05726602566728725 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8475400901507429, 0.8783109819788454, 0.053994227427583176 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8518360359320599, 0.8809954256762437, 0.05900057057293238 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8443200543725675, 0.8826064440735892, 0.05726602566728725 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8475400901507429, 0.8783109819788454, 0.053994227427583176 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8253492860394586, 0.8521834692023548, 0.03445564907390021 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8289187681535768, 0.8502156695601366, 0.0374165252313007 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.814249075089285, 0.8405517602942295, 0.03271757163572124 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8704477401401632, 0.9051547386072125, 0.07594661541529982 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8668712049807323, 0.9025603006327214, 0.0749770142133881 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.857563749911837, 0.8945066893982142, 0.0665663643425036 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8704477401401632, 0.9051547386072125, 0.07594661541529982 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8668712049807323, 0.9025603006327214, 0.0749770142133881 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.857563749911837, 0.8945066893982142, 0.0665663643425036 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.831075076461423, 0.8539730983339536, 0.037807616835715194 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8285609864004464, 0.8502158296414579, 0.037395403745665766 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.814249075089285, 0.8405517602942295, 0.03271757163572124 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8797558363949962, 0.9168759329739509, 0.08055776204938879 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8672289867338631, 0.9104339404417445, 0.0785768069069476 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8564904046524452, 0.895222573067118, 0.06871185160684583 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8797558363949962, 0.9168759329739509, 0.08055776204938879 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8672289867338631, 0.9104339404417445, 0.0785768069069476 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8564904046524452, 0.895222573067118, 0.06871185160684583 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8296413847051506, 0.8545100911262923, 0.03530737681227942 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8271298593879239, 0.8504841259359754, 0.03598346495245956 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.814249075089285, 0.8405517602942295, 0.03271757163572124 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8779650040715307, 0.9201869749832914, 0.08428564390253974 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.871165227204237, 0.9140134788472544, 0.07965076593091318 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8564904046524452, 0.895222573067118, 0.06871185160684583 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8779650040715307, 0.9201869749832914, 0.08428564390253974 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.871165227204237, 0.9140134788472544, 0.07965076593091318 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8564904046524452, 0.895222573067118, 0.06871185160684583 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8292861676957701, 0.8480676183501219, 0.04999375098816713 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8292848853238951, 0.8481568636867529, 0.04725922377726904 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8196209308737441, 0.8419834876117067, 0.039672303396653605 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8568488275915133, 0.8876167893289791, 0.06461754481218701 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8550618423836728, 0.8874377783914229, 0.06632537026353534 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8557767647039964, 0.888064216622044, 0.06709963056644545 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8568488275915133, 0.8876167893289791, 0.06461754481218701 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8550618423836728, 0.8874377783914229, 0.06632537026353534 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8557767647039964, 0.888064216622044, 0.06709963056644545 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8296439494489005, 0.8490519183745343, 0.05082953297589201 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.829287450067645, 0.8469044674694745, 0.0476036138966821 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8196209308737441, 0.8419834876117067, 0.039672303396653605 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8740300459730317, 0.9119548730755224, 0.08126196449300147 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8686607549323228, 0.9126711569477294, 0.08202188015584291 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8740300459730317, 0.9119548730755224, 0.08126196449300147 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8686607549323228, 0.9126711569477294, 0.08202188015584291 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8296439494489005, 0.8512886546365553, 0.04839118424792836 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8314322170286161, 0.851467825655433, 0.041456636700391805 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8196209308737441, 0.8419834876117067, 0.039672303396653605 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8804720410871949, 0.9228716588026717, 0.08543154428575331 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8700918819448452, 0.9174136461322352, 0.08567124055807132 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8804720410871949, 0.9228716588026717, 0.08543154428575331 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8700918819448452, 0.9174136461322352, 0.08567124055807132 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8300004488301564, 0.8514675455131206, 0.04784418221825642 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8325074858458205, 0.8506628967515496, 0.040312668941808114 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8196209308737441, 0.8419834876117067, 0.039672303396653605 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8786824911356044, 0.9305664477554598, 0.08891150538207061 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.87152493251518, 0.9210823898540459, 0.08801286365255921 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8786824911356044, 0.9305664477554598, 0.08891150538207061 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.87152493251518, 0.9210823898540459, 0.08801286365255921 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}


xgb_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='xgbclassifier')
xgb_tuning_accuracy


# filter parameter xgb - accuracy
filter = (xgb_tuning_accuracy['CV Accuracy'] > 0.8) & (xgb_tuning_accuracy['Diff'] > 0)
xgb_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])


# get parameter
params_xgb_accuracy = xgb_tuning_accuracy.loc['param_1006']
params_xgb_accuracy['Parameters']

"{'colsample_bytree': 1.0, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}"


# Define AdaBoostClassifier model and parameters
model = AdaBoostClassifier(random_state=42)
learning_rate = [0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
algorithm = ['SAMME', 'SAMME.R']

# Define grid search
grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, algorithm=algorithm)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")

Best: 0.872244 using {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.6929142542045768, 0.6924660927751296, 0.022290396790059352 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 50}
0.6893357954873334, 0.6955979237452625, 0.019427278743142755 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 100}
0.723326985592552, 0.7269175541174916, 0.022983702303155166 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 150}
0.7329986342739531, 0.7397999383686913, 0.022843292094760735 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 200}
0.776671732035573, 0.7899962781092794, 0.029176806883379917 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 50}
0.7967145632561987, 0.8095028274363376, 0.024384156915168185 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 100}
0.7995768172812434, 0.8185402984716236, 0.02507499853432255 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 150}
0.8078083623469972, 0.8220300712762082, 0.02554976194217574 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 200}
0.7949230897467956, 0.8079818547822294, 0.02532379002983386 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 50}
0.8053019665172704, 0.8220298311542263, 0.028239867597260212 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 100}
0.8174684696815231, 0.8334828092671076, 0.033223842246403554 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 150}
0.8221234795878456, 0.8403720289907273, 0.033551284150194746 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 200}
0.8031571995562994, 0.8162134364257042, 0.023864466986957194 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 50}
0.8253428741800833, 0.8354506889499864, 0.03173676303585906 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 100}
0.8339328421849052, 0.847709556454679, 0.036074851526161335 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 150}
0.8414481825584602, 0.8564784910734653, 0.04005016191334462 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 200}
0.8206897878315733, 0.8360776874652324, 0.0340868434394063 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 50}
0.8464628977757259, 0.8580890292268473, 0.038882707178386246 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 100}
0.85183347118831, 0.8716000528268362, 0.05144847569407908 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 150}
0.8518347535601849, 0.8768794547630195, 0.05330350611944286 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 200}
0.8418130173569033, 0.8589843640569409, 0.040426129931662405 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 50}
0.8511185488679862, 0.8800118860381074, 0.05738634406452961 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 100}
0.8564884810946326, 0.8835910242403141, 0.060681169161536874 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 150}
0.8643609620353807, 0.8864539185906442, 0.0613767439205307 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 200}
0.8493328460320209, 0.8724051418120405, 0.053225232874192376 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}
0.8568494687774507, 0.8840378512284239, 0.06780680730972728 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 100}
0.8657991420932156, 0.891732640181212, 0.07015451355231311 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 150}
0.8683010496213797, 0.8974595494511212, 0.07379998273016547 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 200}
0.6904097819326627, 0.6954188327670456, 0.01824768136217668 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 50}
0.7304935208161014, 0.7372049401095756, 0.020262502511329127 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 100}
0.7469546873897961, 0.7546536040308476, 0.025795815496947108 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 150}
0.7623553324228494, 0.7715641145702017, 0.027664189232246186 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 200}
0.7985002660921641, 0.8095031475989802, 0.024724269128119168 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 50}
0.8103102698751611, 0.8264144985652712, 0.027465015150397558 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}
0.8228364783503567, 0.8413573695437282, 0.03334279687343186 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 150}
0.8314264463551787, 0.8537050421614178, 0.040555611605451555 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 200}
0.8088785016767013, 0.8275781697102127, 0.02345346571473472 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 50}
0.8339328421849052, 0.8556738823322247, 0.04124050430340257 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 100}
0.8428837978725451, 0.8690060550759785, 0.05033212262601407 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 150}
0.8486147177820097, 0.8779539606119909, 0.062058201680318704 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 200}
0.8249850924269528, 0.8443095492510195, 0.03361266003644814 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 50}
0.8457479754554024, 0.8730328206729018, 0.052751905746885104 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 100}
0.8529113047492641, 0.884754375222613, 0.0616095333330887 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 150}
0.8600682221837511, 0.891554269568941, 0.06547038997225749 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 200}
0.8378703650271543, 0.8690956605955826, 0.04667312317080158 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 50}
0.857206609344644, 0.8888699459325335, 0.06825361527914772 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 100}
0.8672283455479255, 0.898533134832495, 0.07061499684419591 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 150}
0.8683004084354422, 0.9042599640617434, 0.07919705569257635 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 200}
0.8457531049429023, 0.8822487823814498, 0.06578808134188835 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 50}
0.8700893172010952, 0.903812976992312, 0.07208159711744441 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 100}
0.8704503048839133, 0.913924313551284, 0.08920646019723158 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 150}
0.8708080866370439, 0.9208139734985373, 0.09482543335119589 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 200}
0.8611422086290803, 0.8884226787207903, 0.06767385021534016 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 50}
0.8700931643167202, 0.9085555862378089, 0.07929995167700161 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.8722437019511288, 0.917771587966687, 0.09380053624971159 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.8718891261276858, 0.92546697722443, 0.10553848227519459 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}


adb_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='adaboost')
adb_tuning_accuracy


# filter parameter - accuracy
filter = (adb_tuning_accuracy['CV Accuracy'] > 0.8) & (adb_tuning_accuracy['Diff'] > 0)
adb_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])


# get parameter
params_adb_accuracy = adb_tuning_accuracy.loc['param_56']
params_adb_accuracy['Parameters']

"{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}"


from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay
params = {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adaboost_model = AdaBoostClassifier(**params, random_state=42)
adaboost_model.fit(X_train, y_train)
y_pred = adaboost_model.predict(X_test)
y_pred_train = adaboost_model.predict(X_train)
print('Classification Report on Test Data')
print(classification_report(y_test, y_pred))
print('\n')
print('Classification Report on Train Data')
print(classification_report(y_train, y_pred_train))

Classification Report on Test Data
              precision    recall  f1-score   support

           0       0.90      0.92      0.91       367
           1       0.53      0.46      0.50        69

    accuracy                           0.85       436
   macro avg       0.72      0.69      0.70       436
weighted avg       0.84      0.85      0.85       436


Classification Report on Train Data
              precision    recall  f1-score   support

           0       0.91      0.93      0.92      1397
           1       0.93      0.91      0.92      1397

    accuracy                           0.92      2794
   macro avg       0.92      0.92      0.92      2794
weighted avg       0.92      0.92      0.92      2794


from sklearn.utils.class_weight import compute_sample_weight
from sklearn.metrics import confusion_matrix

# Compute confusion matrix
cm = confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = cm.ravel()

# Define names for confusion matrix elements
names = ['True Negative', 'False Positive', 'False Negative', 'True Positive']

# Convert counts to string format
counts = ['{0:0.0f}'.format(value) for value in cm.flatten()]

# Create labels with counts and percentages
labels = [f'{v1}\n\n{v2}' for v1, v2 in zip(names, counts)]
labels = np.asarray(labels).reshape(2, 2)

# Create heatmap with annotations
heatmap = sns.heatmap(cm, annot=labels, annot_kws={'size': 13}, fmt='', cmap='coolwarm', center=100)

# Customize tick labels and rotations
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=13)
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=0, ha='right', fontsize=13)

# Set title, ylabel, and xlabel
plt.title(f'Confusion Matrix\nPrecision', fontsize=13, color='black')
plt.ylabel('Actual Label', fontsize=13)
plt.xlabel('\nPredicted Label', fontsize=13)

# Show the plot
plt.show()


# menggunakan data awal
cost = 3  # asumsi cost per customer ($3)
revenue = 11  # asumsi revenue per customer ($11)
total_customer = 2240
total_response = 334
rate_accept = total_response/total_customer * 100
total_cost = total_customer * cost
total_revenue = total_response * revenue
total_profit = total_revenue - total_cost

profit_margin = (total_profit/total_revenue) * 100

print(f'total customer: {total_customer}')
print(f'total response: {total_response}')
print(f'rate accept: {rate_accept:.2f}%')
print(f'total cost: {total_cost}')
print(f'total revenue: {total_revenue}')
print(f'total profit: {total_profit}')
print(f'profit margin before pemodelan: {profit_margin:.2f}%')

total customer: 2240
total response: 334
rate accept: 14.91%
total cost: 6720
total revenue: 3674
total profit: -3046
profit margin before pemodelan: -82.91%


# menggunakan data test - tanpa sample weight
cost = 3  # asumsi cost per customer ($3)
revenue = 11  # asumsi revenue per customer ($11)
total_customer = tp + fp
total_response = tp
rate_accept = total_response/total_customer * 100
total_cost = total_customer * cost
total_revenue = total_response * revenue
total_profit = total_revenue - total_cost

profit_margin = ((total_profit)/total_revenue) * 100

print(f'total customer: {total_customer}')
print(f'total response: {total_response}')
print(f'rate accept: {rate_accept:.2f}%')
print(f'total cost: {total_cost}')
print(f'total revenue: {total_revenue}')
print(f'total profit: {total_profit}')
print(f'profit margin after pemodelan: {profit_margin:.2f}%')

total customer: 60
total response: 32
rate accept: 53.33%
total cost: 180
total revenue: 352
total profit: 172
profit margin after pemodelan: 48.86%


# get clean data from data preprocessing based on selected features in modelling
data_train = pd.merge(data_before_transform_train[selected_features], y_train, left_index=True, right_index=True)
data_test = pd.merge(data_before_transform_test[selected_features], y_test, left_index=True, right_index=True)
data_clean = pd.concat([data_train,data_test])
data_clean['RFM_Cat'] = oe_rfm.inverse_transform(data_clean[['RFM_Cat']])
data_clean['Education'] = oe_edu.inverse_transform(data_clean[['Education']])
data_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2084 entries, 0 to 435
Data columns (total 16 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   AcceptedCmp1         2084 non-null   int64 
 1   AcceptedCmp2         2084 non-null   int64 
 2   AcceptedCmp3         2084 non-null   int64 
 3   AcceptedCmp4         2084 non-null   int64 
 4   AcceptedCmp5         2084 non-null   int64 
 5   Customer_Lifespan    2084 non-null   int64 
 6   Education            2084 non-null   object
 7   MntFishProducts      2084 non-null   int64 
 8   MntFruits            2084 non-null   int64 
 9   MntGoldProds         2084 non-null   int64 
 10  MntSweetProducts     2084 non-null   int64 
 11  NumCatalogPurchases  2084 non-null   int64 
 12  NumWebPurchases      2084 non-null   int64 
 13  RFM_Cat              2084 non-null   object
 14  Recency              2084 non-null   int64 
 15  Response             2084 non-null   int64 
dtypes: int64(14), object(2)
memory usage: 276.8+ KB


data = data_clean.copy()
data.head()


# 10 feature importance based on adaboost model (hyper parameter tuning)
importance_adb.nlargest(10)

NumWebPurchases        0.235
Education              0.175
RFM_Cat                0.125
NumCatalogPurchases    0.100
MntFishProducts        0.070
MntGoldProds           0.070
MntSweetProducts       0.055
Customer_Lifespan      0.050
Recency                0.050
MntFruits              0.030
dtype: float64


# Menghitung frekuensi kategori dan mengurutkannya dari besar ke kecil
rfm_counts = data['RFM_Cat'].value_counts().sort_values(ascending=False)

# Membuat subplot
fig, axes = plt.subplots(1, 2, figsize=(12, 6))

# Plot countplot tanpa bingkai
sns.countplot(x=data['RFM_Cat'], order=rfm_counts.index,
              ax=axes[0], palette=sns.color_palette())
axes[0].set_title('Proportions of Customer Segmentation')

# Menambahkan nilai label pada countplot
for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():0.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
                     ha='center', va='center', xytext=(0, 5), textcoords='offset points')

# Plot pie plot
axes[1].pie(rfm_counts, labels=rfm_counts.index,
            autopct='%1.0f%%', startangle=140, textprops={'fontsize': 12, 'color': 'black'})
axes[1].set_title('Percentages of Customer Segmentation')

plt.tight_layout()
plt.show()

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)


# Menghitung frekuensi kategori dan mengurutkannya dari besar ke kecil
education_counts = data['Education'].value_counts().sort_values(ascending=False)

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

sns.countplot(x=data['Education'], order=education_counts.index, ax=axes[0], palette=sns.color_palette())
axes[0].set_title('Proportions of Customer Education Level')

for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():0.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
                     ha='center', va='center', xytext=(0, 5), textcoords='offset points')

# Plot pie plot
axes[1].pie(education_counts, labels=education_counts.index, autopct='%1.0f%%', startangle=140, textprops={'fontsize': 12, 'color': 'black'})
axes[1].set_title('Percentages of Customer Education Level')

plt.tight_layout()
plt.show()

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)


# 'Education'
education_counts = data.groupby('Education')['Response'].value_counts(normalize=True).unstack()
colors = ['#e41a1c', '#377eb8']

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

education_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[0])
axes[0].set_title('Percentage of Response by Education')
axes[0].set_ylabel('Percentage')
axes[0].set_xticklabels(education_counts.index, rotation=0, ha='center')

handles, labels = axes[0].get_legend_handles_labels()
axes[0].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[0].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[0].patches:
    if p.get_height() < 0.5:
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

# 'RFM_Cat'
rfm_counts = data.groupby('RFM_Cat')['Response'].value_counts(normalize=True).unstack()
rfm_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[1])
axes[1].set_title('Percentage of Response by RFM_Cat')
axes[1].set_ylabel('Percentage')
axes[1].set_xticklabels(rfm_counts.index, rotation=0, ha='center')

handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[1].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[1].patches:
    if p.get_height() <= 0.5:
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

plt.tight_layout()
plt.show()


# graduation education level
filter_graduation = (data['Education'] == 'Graduation') & (data['RFM_Cat'] == 'loyal cust') & (data['Response'] == 1)
filtered_data_graduation = data[filter_graduation][['Customer_Lifespan', 'Recency', 'NumCatalogPurchases', 'NumWebPurchases',
                                                    'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]


from scipy.stats import t

confidence_level = 0.95

# Calculate confidence interval for each variable
print("Confidence Interval Mean of Customer in Graduation Education Level")
for column, values in filtered_data_graduation.items():
    sample_mean = np.mean(values)
    sample_std = np.std(values)
    degrees_of_freedom = len(values) - 1
    alpha = 1 - confidence_level
    t_value = t.ppf(1 - alpha / 2, df=degrees_of_freedom)
    margin_of_error = t_value * (sample_std / np.sqrt(len(values)))
    lower_bound = sample_mean - margin_of_error
    upper_bound = sample_mean + margin_of_error
    print(
        f"Confidence Interval for {column}: ({lower_bound:.2f}, {upper_bound:.2f})")

Confidence Interval Mean of Customer in Graduation Education Level
Confidence Interval for Customer_Lifespan: (393.45, 494.46)
Confidence Interval for Recency: (32.72, 46.01)
Confidence Interval for NumCatalogPurchases: (4.07, 5.64)
Confidence Interval for NumWebPurchases: (4.77, 5.67)
Confidence Interval for MntFishProducts: (53.12, 86.67)
Confidence Interval for MntFruits: (39.56, 63.70)
Confidence Interval for MntGoldProds: (60.24, 92.58)
Confidence Interval for MntSweetProducts: (34.38, 58.68)


# filter apriori
filter_apriori_graduation = (data['Education'] == 'Graduation') & (data['RFM_Cat'] == 'loyal cust') & (data['Response'] == 1)

# web - apriori
web_selling_graduation = data[filter_apriori_graduation][['NumWebPurchases', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]

# catalog - aprioroi
catalog_selling_graduation = data[filter_apriori_graduation][['NumCatalogPurchases', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]


from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

# Encode data dalam format one-hot encoding
web_encoded_graduation = web_selling_graduation.astype(bool).astype(int)
catalog_encoded_graduation = catalog_selling_graduation.astype(bool).astype(int)

# Terapkan algoritma Apriori untuk menemukan item-item yang sering muncul bersama-sama
frequent_itemsets_web_graduation = apriori(web_encoded_graduation, min_support=0.1, use_colnames=True)
frequent_itemsets_catalog_graduation = apriori(catalog_encoded_graduation, min_support=0.1, use_colnames=True)

# Temukan association rules
rules_web_graduation = association_rules(frequent_itemsets_web_graduation, metric="lift", min_threshold=1)
rules_catalog_graduation = association_rules(frequent_itemsets_catalog_graduation, metric="lift", min_threshold=1)

/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(


pd.set_option('display.max_colwidth', 100)
frequent_itemsets_web_graduation[frequent_itemsets_web_graduation['itemsets'].apply(lambda x: 'NumWebPurchases' in x)]

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)


pd.set_option('display.max_colwidth', 100)
frequent_itemsets_catalog_graduation[frequent_itemsets_catalog_graduation['itemsets'].apply(lambda x: 'NumCatalogPurchases' in x)]

/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	...	Year	Total_Purchase	Total_Spending	Total_Offers	Spending_Purchase_Ratio	Deal_Purchase_Ratio	at_least_one_campaign	more_one_campaign	primary_needs	RFM_Cat
0	0.178571	0.0	0.0	0.134665	0.0	0.0	0.494949	-1.523740	-0.004248	-1.040166	...	0.5	0.119048	0.016687	1.932145	-0.991174	0.166667	2.416231	-0.256859	0.0	0.333333
1	0.410714	0.5	0.0	0.557668	0.0	0.5	0.303030	0.756515	0.356628	0.698862	...	0.5	0.547619	0.324593	-0.512632	0.471277	0.125000	-0.413868	-0.256859	1.0	0.666667
2	0.553571	1.0	0.5	0.627112	0.0	0.5	0.595960	1.453882	-1.447016	0.121258	...	0.0	0.428571	0.428288	1.932145	1.036242	0.157895	2.416231	-0.256859	1.0	0.666667
3	0.500000	1.0	0.5	0.245143	0.5	0.0	0.434343	-0.859859	-1.447016	-1.365005	...	0.5	0.095238	0.010330	-0.512632	-1.182550	0.200000	-0.413868	-0.256859	1.0	0.333333
4	0.517857	0.0	0.5	0.024088	0.0	0.5	0.000000	-1.062296	-0.380042	-0.991477	...	0.5	0.571429	0.139452	-0.512632	-0.453045	0.000000	-0.413868	-0.256859	1.0	0.666667

	CV Precision	Precision_Train	Precision_Test	Diff	Diff (%)
SVC	0.799378	0.856226	0.836876	0.019350	2.259904
XGBClassifier	0.847233	0.864785	0.827637	0.037148	4.295622
AdaBoostClassifier	0.872463	0.879967	0.822993	0.056974	6.474576

	CV Accuracy	Accuracy_Train	Accuracy_Test	Diff	Diff (%)
SVC	0.832502	0.852899	0.743119	0.109780	12.871371
XGBClassifier	0.840023	0.864710	0.807339	0.057371	6.634668
AdaBoostClassifier	0.861142	0.879742	0.816514	0.063229	7.187166

	Model	CV Precision	Precision_Train	Precision_Test	Diff	Diff (%)	Parameters
Parameter Set
param_1	SVC	0.864495	0.814202	0.804183	0.010019	1.230524	{'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
param_2	SVC	0.799378	0.856226	0.836876	0.019350	2.259904	{'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
param_3	SVC	0.792250	0.807795	0.846378	-0.038583	-4.776376	{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
param_4	SVC	0.864806	0.817847	0.804183	0.013664	1.670685	{'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
param_5	SVC	0.800823	0.857977	0.833500	0.024477	2.852910	{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
param_6	SVC	0.792250	0.807795	0.846378	-0.038583	-4.776376	{'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
param_7	SVC	0.869762	0.832848	0.811858	0.020990	2.520239	{'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
param_8	SVC	0.818455	0.876420	0.821935	0.054485	6.216769	{'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
param_9	SVC	0.790871	0.807753	0.847020	-0.039267	-4.861221	{'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
param_10	SVC	0.873749	0.837537	0.814110	0.023427	2.797151	{'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
param_11	SVC	0.822184	0.878579	0.825903	0.052676	5.995626	{'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
param_12	SVC	0.790871	0.807753	0.847020	-0.039267	-4.861221	{'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
param_13	SVC	0.874557	0.840083	0.810913	0.029170	3.472247	{'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
param_14	SVC	0.829484	0.888494	0.825154	0.063340	7.128920	{'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
param_15	SVC	0.790577	0.808051	0.846378	-0.038327	-4.743093	{'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
param_16	SVC	0.874959	0.844516	0.807559	0.036957	4.376101	{'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
param_17	SVC	0.830412	0.888974	0.820530	0.068444	7.699262	{'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
param_18	SVC	0.790577	0.808051	0.846378	-0.038327	-4.743093	{'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
param_19	SVC	0.871936	0.847879	0.810913	0.036966	4.359848	{'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
param_20	SVC	0.836304	0.896172	0.825212	0.070960	7.918164	{'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
param_21	SVC	0.789915	0.807673	0.846378	-0.038705	-4.792119	{'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
param_22	SVC	0.871539	0.852063	0.806533	0.045530	5.343496	{'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
param_23	SVC	0.838417	0.895851	0.821919	0.073932	8.252699	{'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
param_24	SVC	0.789915	0.807673	0.846378	-0.038705	-4.792119	{'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
param_25	SVC	0.870785	0.853597	0.809886	0.043711	5.120801	{'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
param_26	SVC	0.840256	0.898973	0.822615	0.076358	8.493901	{'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
param_27	SVC	0.791099	0.808051	0.846378	-0.038327	-4.743093	{'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
param_28	SVC	0.871803	0.857475	0.816533	0.040941	4.774655	{'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
param_29	SVC	0.843025	0.901236	0.822615	0.078621	8.723685	{'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
param_30	SVC	0.791099	0.808051	0.846378	-0.038327	-4.743093	{'C': 5, 'gamma': 'auto', 'kernel': 'linear'}

	Model	CV Precision	Precision_Train	Precision_Test	Diff	Diff (%)	Parameters
Parameter Set
param_1	XGBClassifier	0.825169	0.850661	0.835631	0.015030	1.766848	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_2	XGBClassifier	0.828413	0.845522	0.833142	0.012380	1.464172	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_3	XGBClassifier	0.826268	0.840944	0.827472	0.013472	1.601998	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_4	XGBClassifier	0.820744	0.837713	0.827491	0.010222	1.220218	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_5	XGBClassifier	0.816850	0.836493	0.829966	0.006527	0.780327	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
...	...	...	...	...	...	...	...
param_1292	XGBClassifier	0.900328	0.915380	0.840051	0.075329	8.229262	{'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1293	XGBClassifier	0.878870	0.895441	0.823811	0.071630	7.999425	{'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1294	XGBClassifier	0.905373	0.922569	0.819691	0.102878	11.151284	{'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1295	XGBClassifier	0.900328	0.915380	0.840051	0.075329	8.229262	{'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1296	XGBClassifier	0.878870	0.895441	0.823811	0.071630	7.999425	{'colsample_bytree': 1.0, 'gamma': 3, 'learnin...

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumWebVisitsMonth	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	2012-09-04	58	635	...	7	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	2014-03-08	38	11	...	5	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	2013-08-21	26	426	...	4	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	2014-02-10	26	11	...	6	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	2014-01-19	94	173	...	5	3	11	0

	count	mean	std	min	10%	20%	30%	40%	50%	60%	70%	80%	90%	max
Year_Birth	2240.0	1968.805804	11.984069	1893.0	1952.0	1957.0	1962.0	1966.0	1970.0	1973.0	1976.0	1979.0	1984.0	1996.0
Income	2216.0	52247.251354	25173.076661	1730.0	24117.5	32011.0	38198.5	44529.0	51381.5	58482.0	65247.5	71819.0	79844.0	666666.0
Kidhome	2240.0	0.444196	0.538398	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	2.0
Teenhome	2240.0	0.506250	0.544538	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	2.0
Recency	2240.0	49.109375	28.962453	0.0	9.0	19.0	29.0	39.0	49.0	59.0	69.0	79.0	89.0	99.0
MntWines	2240.0	303.935714	336.597393	0.0	6.0	16.0	34.0	81.0	173.5	284.4	418.6	581.2	822.1	1493.0
MntFruits	2240.0	26.302232	39.773434	0.0	0.0	1.0	2.0	4.0	8.0	15.0	25.0	44.0	83.0	199.0
MntMeatProducts	2240.0	166.950000	225.715373	0.0	7.0	12.0	20.0	35.0	67.0	108.4	177.0	298.4	499.0	1725.0
MntFishProducts	2240.0	37.525446	54.628979	0.0	0.0	2.0	3.0	7.0	12.0	20.0	37.0	65.0	120.0	259.0
MntSweetProducts	2240.0	27.062946	41.280498	0.0	0.0	1.0	2.0	5.0	8.0	14.0	26.0	44.2	89.0	263.0
MntGoldProds	2240.0	44.021875	52.167439	0.0	3.0	6.0	11.0	17.0	24.0	34.0	46.0	73.0	122.0	362.0
NumDealsPurchases	2240.0	2.325000	1.932238	0.0	1.0	1.0	1.0	1.0	2.0	2.0	3.0	3.0	5.0	15.0
NumWebPurchases	2240.0	4.084821	2.778714	0.0	1.0	2.0	2.0	3.0	4.0	4.0	5.0	6.0	8.0	27.0
NumCatalogPurchases	2240.0	2.662054	2.923101	0.0	0.0	0.0	1.0	1.0	2.0	2.0	4.0	5.0	7.0	28.0
NumStorePurchases	2240.0	5.790179	3.250958	0.0	2.0	3.0	3.0	4.0	5.0	6.0	7.0	9.0	11.0	13.0
NumWebVisitsMonth	2240.0	5.316518	2.426645	0.0	2.0	3.0	4.0	5.0	6.0	6.0	7.0	7.0	8.0	20.0
AcceptedCmp3	2240.0	0.072768	0.259813	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
AcceptedCmp4	2240.0	0.074554	0.262728	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
AcceptedCmp5	2240.0	0.072768	0.259813	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
AcceptedCmp1	2240.0	0.064286	0.245316	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
AcceptedCmp2	2240.0	0.013393	0.114976	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
Complain	2240.0	0.009375	0.096391	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
Response	2240.0	0.149107	0.356274	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0

	Count	Yes Response	Success Rate
Cmp1	144	79	54.86
Cmp2	30	20	66.67
Cmp3	163	77	47.24
Cmp4	167	62	37.13
Cmp5	163	92	56.44
Any campaigns	463	334	72.14
Total Response	2240	334	14.91

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	...	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Response
1107	1970	2n Cycle	Married	15315.0	0	0	2013-08-03	27	7	4	...	0	4	5	0	0	0	0	0	0	0
558	1970	2n Cycle	Married	15315.0	0	0	2013-08-03	27	7	4	...	0	4	5	0	0	0	0	0	0	0
1549	1975	2n Cycle	Married	37284.0	1	1	2013-03-29	46	11	1	...	0	3	6	0	0	0	0	0	0	0
2015	1975	2n Cycle	Married	37284.0	1	1	2013-03-29	46	11	1	...	0	3	6	0	0	0	0	0	0	0
669	1971	2n Cycle	Married	54690.0	1	1	2013-11-07	76	111	16	...	1	5	3	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2062	1982	PhD	Together	70038.0	0	0	2013-10-25	54	587	54	...	4	8	2	0	0	0	0	0	0	0
1630	1947	PhD	Together	73059.0	0	1	2013-08-31	36	410	112	...	3	13	4	0	0	0	0	0	0	0
658	1947	PhD	Together	73059.0	0	1	2013-08-31	36	410	112	...	3	13	4	0	0	0	0	0	0	0
1649	1950	PhD	Widow	56551.0	1	1	2014-05-07	48	67	4	...	1	4	4	0	0	0	0	0	0	0
1959	1950	PhD	Widow	56551.0	1	1	2014-05-07	48	67	4	...	1	4	4	0	0	0	0	0	0	0

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	...	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp5
508	1992	Graduation	Married	34935.0	0	0	2013-06-21	71	34	4	...	1	4	7	0	0
1578	1992	Graduation	Married	34935.0	0	0	2013-06-21	71	34	4	...	1	4	7	0	0
1318	1972	Graduation	Married	40321.0	1	1	2013-07-29	59	44	4	...	0	3	7	0	0
316	1972	Graduation	Married	40321.0	1	1	2013-07-29	59	44	4	...	0	3	7	0	0
705	1986	Graduation	Married	71952.0	1	0	2013-01-10	93	656	80	...	4	8	4	1	1
351	1986	Graduation	Married	71952.0	1	0	2013-01-10	93	656	80	...	4	8	4	1	1
1822	1971	Master	Single	33316.0	1	1	2013-10-04	34	79	1	...	1	4	6	0	0
1359	1971	Master	Single	33316.0	1	1	2013-10-04	34	79	1	...	1	4	6	0	0
1545	1986	Master	Together	42386.0	1	0	2013-01-13	43	65	4	...	0	3	8	0	0
297	1986	Master	Together	42386.0	1	0	2013-01-13	43	65	4	...	0	3	8	0	0
1347	1983	PhD	Married	50150.0	0	0	2013-06-20	32	135	46	...	2	7	5	0	0
99	1983	PhD	Married	50150.0	0	0	2013-06-20	32	135	46	...	2	7	5	0	0

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	...	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth
0	1977	Graduation	Together	666666.0	1	0	2013-06-02	23	9	14	...	1	3	6
5	1982	PhD	Married	160803.0	0	0	2012-08-04	21	55	16	...	28	1	0
6	1971	Master	Together	157733.0	1	0	2013-06-04	37	39	1	...	0	1	1
1	1973	PhD	Married	157243.0	0	1	2014-03-01	98	20	2	...	22	0	0
2	1977	Graduation	Together	157146.0	0	0	2013-04-29	13	1	0	...	28	0	1
3	1949	PhD	Married	156924.0	0	0	2013-08-29	85	2	1	...	0	0	0
4	1975	Graduation	Divorced	153924.0	0	0	2014-02-07	81	1	1	...	0	0	0

	Skewness	Type of Distribution	Normalization Advice
Feature
AcceptedCmp1	3.531	Highly Positively Skewed	PowerTransformer/Log
AcceptedCmp2	8.911	Highly Positively Skewed	PowerTransformer/Log
AcceptedCmp3	3.188	Highly Positively Skewed	PowerTransformer/Log
AcceptedCmp4	3.341	Highly Positively Skewed	PowerTransformer/Log
AcceptedCmp5	3.254	Highly Positively Skewed	PowerTransformer/Log
Complain	9.151	Highly Positively Skewed	PowerTransformer/Log
Customer_Lifespan	-0.011	Normal Distribution (Symmetric)	No Transformation
Deal_Purchase_Ratio	0.831	Positively Skewed	MinMaxScaler
Education	0.628	Positively Skewed	MinMaxScaler
Family_Size	0.080	Normal Distribution (Symmetric)	No Transformation
Income	0.023	Normal Distribution (Symmetric)	No Transformation
Kidhome	0.615	Positively Skewed	MinMaxScaler
Marital_Status	0.012	Normal Distribution (Symmetric)	No Transformation
MntFishProducts	1.907	Highly Positively Skewed	PowerTransformer/Log
MntFruits	2.062	Highly Positively Skewed	PowerTransformer/Log
MntGoldProds	1.877	Highly Positively Skewed	PowerTransformer/Log
MntMeatProducts	1.836	Highly Positively Skewed	PowerTransformer/Log
MntSweetProducts	2.082	Highly Positively Skewed	PowerTransformer/Log
MntWines	1.223	Highly Positively Skewed	PowerTransformer/Log
NumCatalogPurchases	1.445	Highly Positively Skewed	PowerTransformer/Log
NumDealsPurchases	2.415	Highly Positively Skewed	PowerTransformer/Log
NumStorePurchases	0.722	Positively Skewed	MinMaxScaler
NumWebPurchases	1.386	Highly Positively Skewed	PowerTransformer/Log
NumWebVisitsMonth	0.367	Negatively Skewed	MinMaxScaler
RFM_Cat	-0.035	Normal Distribution (Symmetric)	No Transformation
Recency	0.014	Normal Distribution (Symmetric)	No Transformation
Relationship_Status	-0.640	Positively Skewed	MinMaxScaler
Spending_Purchase_Ratio	1.333	Highly Positively Skewed	PowerTransformer/Log
Teenhome	0.412	Positively Skewed	MinMaxScaler
Total_Children	0.407	Positively Skewed	MinMaxScaler
Total_Offers	2.681	Highly Positively Skewed	PowerTransformer/Log
Total_Purchase	0.244	Positively Skewed	MinMaxScaler
Total_Spending	0.885	Positively Skewed	MinMaxScaler
Year	-0.043	Normal Distribution (Symmetric)	No Transformation
Year_Birth	-0.088	Normal Distribution (Symmetric)	No Transformation
at_least_one_campaign	2.002	Highly Positively Skewed	PowerTransformer/Log
more_one_campaign	3.636	Highly Positively Skewed	PowerTransformer/Log
primary_needs	-1.251	Positively Skewed	MinMaxScaler

	Feature	VIF
0	Income	3.000721e+01
1	Kidhome	inf
2	Teenhome	inf
3	Recency	7.906623e+00
4	MntWines	1.536519e+01
5	MntFruits	2.799495e+00
6	MntMeatProducts	1.068134e+01
7	MntFishProducts	2.946718e+00
8	MntSweetProducts	2.689423e+00
9	MntGoldProds	2.795796e+00
10	NumWebPurchases	4.477282e+00
11	NumCatalogPurchases	6.486516e+00
12	AcceptedCmp3	4.820741e+01
13	AcceptedCmp4	4.646552e+01
14	AcceptedCmp5	4.511825e+01
15	AcceptedCmp1	4.010818e+01
16	AcceptedCmp2	5.816116e+00
17	Total_Children	inf
18	Family_Size	inf
19	Customer_Lifespan	1.606089e+01
20	Year	1.268764e+01
21	Total_Purchase	5.257854e+01
22	Total_Spending	1.959583e+01
23	Total_Offers	2.266641e+07
24	Spending_Purchase_Ratio	2.694432e+01
25	Deal_Purchase_Ratio	1.233535e+01
26	at_least_one_campaign	1.684281e+07
27	more_one_campaign	8.183600e+06
28	Education	2.049095e+00
29	Relationship_Status	inf
30	RFM_Cat	2.594126e+01

	Feature	VIF
0	Recency	2.562749
1	MntFruits	2.510120
2	MntFishProducts	2.662851
3	MntSweetProducts	2.489294
4	MntGoldProds	2.186144
5	NumWebPurchases	1.853393
6	NumCatalogPurchases	3.031351
7	AcceptedCmp3	1.103208
8	AcceptedCmp4	1.280283
9	AcceptedCmp5	1.372765
10	AcceptedCmp1	1.306924
11	AcceptedCmp2	1.119148
12	Customer_Lifespan	4.230985
13	Education	1.960772
14	RFM_Cat	4.520092

	Model	CV Precision	Precision_Train	Precision_Test	Diff	Diff (%)	Parameters
Parameter Set
param_140	XGBClassifier	0.912467	0.922357	0.818055	0.104302	11.308190	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_143	XGBClassifier	0.912467	0.922357	0.818055	0.104302	11.308190	{'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_1147	XGBClassifier	0.912229	0.924031	0.835093	0.088938	9.625034	{'colsample_bytree': 1.0, 'gamma': 2, 'learnin...

	Recency	Frequency	Monetary
0	49	6	50
1	30	24	825
2	59	19	1086
3	43	5	34
4	0	25	359

	Model	CV Precision	Precision_Train	Precision_Test	Diff	Diff (%)	Parameters
Parameter Set
param_1	AdaBoostClassifier	0.649213	0.711296	0.802507	-0.091211	-12.823232	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_2	AdaBoostClassifier	0.648167	0.711701	0.790427	-0.078726	-11.061692	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_3	AdaBoostClassifier	0.697819	0.736949	0.797057	-0.060109	-8.156405	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_4	AdaBoostClassifier	0.714342	0.737002	0.793539	-0.056537	-7.671175	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_5	AdaBoostClassifier	0.774103	0.781595	0.807536	-0.025941	-3.318927	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_6	AdaBoostClassifier	0.792092	0.802792	0.810650	-0.007859	-0.978912	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_7	AdaBoostClassifier	0.789720	0.820168	0.817211	0.002957	0.360545	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_8	AdaBoostClassifier	0.798189	0.825283	0.823851	0.001432	0.173518	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_9	AdaBoostClassifier	0.792266	0.810844	0.812586	-0.001742	-0.214828	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_10	AdaBoostClassifier	0.792671	0.816348	0.824742	-0.008394	-1.028224	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_11	AdaBoostClassifier	0.805303	0.835488	0.832016	0.003472	0.415603	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_12	AdaBoostClassifier	0.808808	0.842031	0.828284	0.013747	1.632651	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_13	AdaBoostClassifier	0.802750	0.817125	0.816566	0.000559	0.068402	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_14	AdaBoostClassifier	0.811530	0.837601	0.824233	0.013368	1.595944	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_15	AdaBoostClassifier	0.823586	0.847405	0.824233	0.023172	2.734462	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_16	AdaBoostClassifier	0.833255	0.855825	0.823515	0.032309	3.775241	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_17	AdaBoostClassifier	0.810949	0.833202	0.826708	0.006493	0.779341	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_18	AdaBoostClassifier	0.838313	0.857308	0.819657	0.037652	4.391845	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_19	AdaBoostClassifier	0.852054	0.871906	0.815523	0.056383	6.466587	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_20	AdaBoostClassifier	0.855449	0.876164	0.821865	0.054299	6.197341	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_21	AdaBoostClassifier	0.838551	0.855509	0.815478	0.040032	4.679263	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_22	AdaBoostClassifier	0.860879	0.880849	0.810752	0.070097	7.957845	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_23	AdaBoostClassifier	0.866334	0.887425	0.824434	0.062991	7.098191	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_24	AdaBoostClassifier	0.878023	0.891516	0.832926	0.058590	6.571986	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_25	AdaBoostClassifier	0.849212	0.871961	0.819175	0.052785	6.053645	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_26	AdaBoostClassifier	0.863298	0.882214	0.808512	0.073702	8.354178	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_27	AdaBoostClassifier	0.882157	0.887904	0.816892	0.071012	7.997676	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_28	AdaBoostClassifier	0.882771	0.893509	0.828283	0.065227	7.300039	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_29	AdaBoostClassifier	0.649094	0.711996	0.790427	-0.078431	-11.015625	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_30	AdaBoostClassifier	0.709983	0.737859	0.790033	-0.052173	-7.070882	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_31	AdaBoostClassifier	0.722910	0.752078	0.796505	-0.044426	-5.907148	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_32	AdaBoostClassifier	0.742369	0.769917	0.798014	-0.028098	-3.649448	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_33	AdaBoostClassifier	0.785239	0.810770	0.813240	-0.002470	-0.304647	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_34	AdaBoostClassifier	0.801763	0.818932	0.816745	0.002187	0.267035	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_35	AdaBoostClassifier	0.818360	0.836725	0.820971	0.015754	1.882816	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_36	AdaBoostClassifier	0.829634	0.850601	0.823515	0.027086	3.184370	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_37	AdaBoostClassifier	0.799590	0.823803	0.826708	-0.002906	-0.352699	{'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_38	AdaBoostClassifier	0.833774	0.851398	0.822653	0.028745	3.376218	{'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_39	AdaBoostClassifier	0.849246	0.866599	0.824764	0.041835	4.827492	{'algorithm': 'SAMME.R', 'learning_rate': 0.2,...

	Model	CV Accuracy	Accuracy_Train	Accuracy_Test	Diff	Diff (%)	Parameters
Parameter Set
param_1	AdaBoostClassifier	0.692914	0.692198	0.527523	0.164675	23.790120	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_2	AdaBoostClassifier	0.689336	0.694345	0.525229	0.169116	24.356143	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_3	AdaBoostClassifier	0.723327	0.734431	0.651376	0.083055	11.308725	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_4	AdaBoostClassifier	0.732999	0.734789	0.649083	0.085706	11.664067	{'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_5	AdaBoostClassifier	0.776672	0.780601	0.715596	0.065005	8.327549	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_6	AdaBoostClassifier	0.796715	0.802792	0.745413	0.057379	7.147415	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_7	AdaBoostClassifier	0.799577	0.818898	0.740826	0.078072	9.533786	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_8	AdaBoostClassifier	0.807808	0.823193	0.745413	0.077780	9.448544	{'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_9	AdaBoostClassifier	0.794923	0.810308	0.743119	0.067189	8.291730	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_10	AdaBoostClassifier	0.805302	0.816034	0.766055	0.049979	6.124658	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_11	AdaBoostClassifier	0.817468	0.834646	0.772936	0.061710	7.393543	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_12	AdaBoostClassifier	0.822123	0.841804	0.784404	0.057400	6.818714	{'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_13	AdaBoostClassifier	0.803157	0.816750	0.747706	0.069044	8.453473	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_14	AdaBoostClassifier	0.825343	0.837151	0.779817	0.057335	6.848767	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_15	AdaBoostClassifier	0.833933	0.846457	0.779817	0.066640	7.872840	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_16	AdaBoostClassifier	0.841448	0.855404	0.791284	0.064120	7.495873	{'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_17	AdaBoostClassifier	0.820690	0.832856	0.779817	0.053040	6.368400	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_18	AdaBoostClassifier	0.846463	0.857194	0.793578	0.063616	7.421425	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_19	AdaBoostClassifier	0.851833	0.871868	0.800459	0.071410	8.190408	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_20	AdaBoostClassifier	0.851835	0.876163	0.814220	0.061943	7.069804	{'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_21	AdaBoostClassifier	0.841813	0.855404	0.788991	0.066414	7.764001	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_22	AdaBoostClassifier	0.851119	0.880816	0.805046	0.075770	8.602269	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_23	AdaBoostClassifier	0.856488	0.887258	0.823394	0.063864	7.197893	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_24	AdaBoostClassifier	0.864361	0.891195	0.834862	0.056333	6.321064	{'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_25	AdaBoostClassifier	0.849333	0.871868	0.798165	0.073703	8.453473	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_26	AdaBoostClassifier	0.856849	0.881532	0.809633	0.071899	8.156123	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_27	AdaBoostClassifier	0.865799	0.887258	0.821101	0.066157	7.456395	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_28	AdaBoostClassifier	0.868301	0.892985	0.830275	0.062710	7.022485	{'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_29	AdaBoostClassifier	0.690410	0.694703	0.525229	0.169474	24.395115	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_30	AdaBoostClassifier	0.730494	0.735863	0.646789	0.089074	12.104648	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_31	AdaBoostClassifier	0.746955	0.748389	0.649083	0.099307	13.269407	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_32	AdaBoostClassifier	0.762355	0.767359	0.678899	0.088460	11.527797	{'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_33	AdaBoostClassifier	0.798500	0.809592	0.736239	0.073353	9.060546	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_34	AdaBoostClassifier	0.810310	0.818540	0.756881	0.061659	7.532804	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_35	AdaBoostClassifier	0.822836	0.836435	0.777523	0.058912	7.043257	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_36	AdaBoostClassifier	0.831426	0.850394	0.791284	0.059109	6.950815	{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_37	AdaBoostClassifier	0.808879	0.823550	0.779817	0.043734	5.310415	{'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_38	AdaBoostClassifier	0.833933	0.851110	0.788991	0.062119	7.298555	{'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_39	AdaBoostClassifier	0.842884	0.866500	0.800459	0.066041	7.621576	{'algorithm': 'SAMME.R', 'learning_rate': 0.2,...

	support	itemsets
0	1.000000	(NumWebPurchases)
5	0.897059	(NumWebPurchases, MntFishProducts)
6	0.926471	(MntFruits, NumWebPurchases)
7	0.955882	(NumWebPurchases, MntGoldProds)
8	0.823529	(NumWebPurchases, MntSweetProducts)
15	0.852941	(MntFruits, NumWebPurchases, MntFishProducts)
16	0.882353	(MntGoldProds, NumWebPurchases, MntFishProducts)
17	0.794118	(MntSweetProducts, NumWebPurchases, MntFishProducts)
18	0.882353	(MntFruits, NumWebPurchases, MntGoldProds)
19	0.794118	(MntFruits, NumWebPurchases, MntSweetProducts)
20	0.808824	(MntSweetProducts, NumWebPurchases, MntGoldProds)
25	0.838235	(MntGoldProds, MntFruits, NumWebPurchases, MntFishProducts)
26	0.764706	(MntFruits, MntSweetProducts, NumWebPurchases, MntFishProducts)
27	0.779412	(MntGoldProds, MntSweetProducts, NumWebPurchases, MntFishProducts)
28	0.779412	(MntFruits, MntSweetProducts, NumWebPurchases, MntGoldProds)
30	0.750000	(MntSweetProducts, NumWebPurchases, MntFishProducts, MntFruits, MntGoldProds)

	Model	CV Precision	Precision_Train	Precision_Test	Diff	Diff (%)	Parameters
Parameter Set
param_56	AdaBoostClassifier	0.908271	0.918675	0.843316	0.075359	8.203003	{'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_55	AdaBoostClassifier	0.903253	0.910859	0.831003	0.079856	8.767125	{'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_52	AdaBoostClassifier	0.900182	0.912634	0.822355	0.090279	9.892101	{'algorithm': 'SAMME.R', 'learning_rate': 0.8,...

	Model	CV Accuracy	Accuracy_Train	Accuracy_Test	Diff	Diff (%)	Parameters
Parameter Set
param_1	SVC	0.771293	0.796349	0.795872	0.000478	0.059994	{'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
param_2	SVC	0.832502	0.852899	0.743119	0.109780	12.871371	{'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
param_3	SVC	0.806012	0.807087	0.768349	0.038738	4.799731	{'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
param_4	SVC	0.777734	0.800644	0.795872	0.004773	0.596105	{'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
param_5	SVC	0.834291	0.854331	0.740826	0.113505	13.285841	{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
param_6	SVC	0.806012	0.807087	0.768349	0.038738	4.799731	{'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
param_7	SVC	0.794916	0.818898	0.807339	0.011558	1.411433	{'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
param_8	SVC	0.850756	0.874374	0.738532	0.135842	15.535869	{'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
param_9	SVC	0.804579	0.807087	0.770642	0.036444	4.515552	{'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
param_10	SVC	0.801000	0.824266	0.807339	0.016927	2.053564	{'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
param_11	SVC	0.853261	0.876521	0.743119	0.133402	15.219468	{'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
param_12	SVC	0.804579	0.807087	0.770642	0.036444	4.515552	{'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
param_13	SVC	0.804220	0.827845	0.800459	0.027387	3.308186	{'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
param_14	SVC	0.860061	0.886543	0.750000	0.136543	15.401696	{'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
param_15	SVC	0.804936	0.807445	0.768349	0.039096	4.841930	{'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
param_16	SVC	0.807083	0.832856	0.798165	0.034691	4.165303	{'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
param_17	SVC	0.861493	0.886901	0.743119	0.143781	16.211653	{'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
param_18	SVC	0.804936	0.807445	0.768349	0.039096	4.841930	{'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
param_19	SVC	0.807799	0.837867	0.800459	0.037408	4.464688	{'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
param_20	SVC	0.866503	0.894417	0.759174	0.135242	15.120727	{'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
param_21	SVC	0.804221	0.807087	0.768349	0.038738	4.799731	{'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
param_22	SVC	0.811378	0.842520	0.795872	0.046648	5.536740	{'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
param_23	SVC	0.867577	0.894059	0.756881	0.137178	15.343284	{'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
param_24	SVC	0.804221	0.807087	0.768349	0.038738	4.799731	{'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
param_25	SVC	0.811377	0.844667	0.798165	0.046502	5.505365	{'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
param_26	SVC	0.869367	0.897280	0.759174	0.138106	15.391582	{'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
param_27	SVC	0.805294	0.807445	0.768349	0.039096	4.841930	{'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
param_28	SVC	0.817103	0.849320	0.802752	0.046568	5.482937	{'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
param_29	SVC	0.872946	0.899785	0.759174	0.140611	15.627167	{'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
param_30	SVC	0.805294	0.807445	0.768349	0.039096	4.841930	{'C': 5, 'gamma': 'auto', 'kernel': 'linear'}

	Model	CV Accuracy	Accuracy_Train	Accuracy_Test	Diff	Diff (%)	Parameters
Parameter Set
param_1003	XGBClassifier	0.879048	0.924123	0.850917	0.073206	7.921638	{'colsample_bytree': 1.0, 'gamma': 1, 'learnin...
param_1006	XGBClassifier	0.879048	0.924123	0.850917	0.073206	7.921638	{'colsample_bytree': 1.0, 'gamma': 1, 'learnin...
param_247	XGBClassifier	0.878325	0.918754	0.837156	0.081599	8.881427	{'colsample_bytree': 0.8, 'gamma': 2, 'learnin...

	AcceptedCmp3	AcceptedCmp4	Customer_Lifespan	Education	MntFishProducts	MntFruits	MntGoldProds	MntSweetProducts	NumCatalogPurchases	NumWebPurchases	RFM_Cat	Recency
0	1	0	347	Graduation	12	8	13	2	0	2	at risk cust	49
1	0	0	525	Master	151	15	15	38	3	5	loyal cust	30
2	0	1	623	PhD	0	0	10	0	4	3	loyal cust	59
3	0	0	418	PhD	0	0	2	0	0	1	at risk cust	43
4	0	0	269	Graduation	2	4	321	4	0	25	loyal cust	0

	support	itemsets
0	1.000000	(NumCatalogPurchases)
5	0.897059	(NumCatalogPurchases, MntFishProducts)
6	0.926471	(MntFruits, NumCatalogPurchases)
7	0.955882	(NumCatalogPurchases, MntGoldProds)
8	0.823529	(NumCatalogPurchases, MntSweetProducts)
15	0.852941	(MntFruits, NumCatalogPurchases, MntFishProducts)
16	0.882353	(MntGoldProds, NumCatalogPurchases, MntFishProducts)
17	0.794118	(NumCatalogPurchases, MntSweetProducts, MntFishProducts)
18	0.882353	(MntFruits, NumCatalogPurchases, MntGoldProds)
19	0.794118	(MntFruits, NumCatalogPurchases, MntSweetProducts)
20	0.808824	(NumCatalogPurchases, MntSweetProducts, MntGoldProds)
25	0.838235	(MntGoldProds, MntFruits, NumCatalogPurchases, MntFishProducts)
26	0.764706	(MntFruits, NumCatalogPurchases, MntSweetProducts, MntFishProducts)
27	0.779412	(MntGoldProds, NumCatalogPurchases, MntSweetProducts, MntFishProducts)
28	0.779412	(MntFruits, NumCatalogPurchases, MntSweetProducts, MntGoldProds)
30	0.750000	(MntSweetProducts, NumCatalogPurchases, MntFishProducts, MntFruits, MntGoldProds)

Market Insider - Data Science Batch 38¶

Anggota:¶

Table of Contents

1. Introduction

Problem Statement¶

Attributes¶

Acknowledgements¶

Inspiration¶

2. Imports Dataset

3. EDA + Business Insights

3.1 Data Overview

3.2 Statistical Analysis

Insight Data Overview & Statistical Analysis¶

3.3 Univariate Analysis

3.3.1 Distribution of Numerical Feature

Insight Distribusi Data:¶

Insight Box Plot:¶

3.3.2 Distribution of Categorical Feature

Insight Countplot/Barplot (Categorical Feature):¶

Insight Percentage of Response (Categorical Feature vs Target):¶

3.2.3 Distribution of Datetime Feature

Insight (Datetime Feature):¶

Insight (Datetime Feature vs Target):¶

3.4 Multivariate Analysis

3.4.1 Based on Regression Line

Insight Regression Plot (Numerical Feature vs Target):¶

3.4.2 Based on Correlation Coefficient (Heatmap)

Insight Correlation of Coefficient (Heatmap):¶

3.5 Business Insights

Summary Business Insights¶

4. Data Pre-Processing

4.1 Data Split

4.2 Handling Missing Values

4.3 Handling Duplicates Values

4.4 Handling Outliers

4.5 Handling Invalid Values

4.6 Feature Extraction

4.7 Feature Encoding

4.8 Feature Transformation

4.9 Feature Selection

4.9.1 Based on Anova (Numerical-Categorical)

4.9.2 Based on Chi-Square (Categorical-Categorical)

4.9.3 Redundancy Analysis

Variance Inflation Factor (VIF)¶

Langkah-langkah Feature Selection dengan VIF:¶

4.10 Imbalance Handling

5. Machine Learning Modelling & Evaluation

5.1 Modelling

Default Parameter¶

Hyperparameter Tuning¶

Precision¶

SVM¶

XGB Classifier¶

AdaBoost Classifier¶

Accuracy¶

SVM¶

XGB Classifier¶

Adaboost Classifier¶

5.2 Business Recommendation

5.2.1 Business Simulation¶

5.2.2 Business Insights & Recommendation¶

Business Insights¶

Business Recommendation¶